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colm2026-0001 | FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing | null | [
"Wang Wei",
"Hongzheng Yang",
"Tiankai Yang",
"Samyadeep Basu",
"Hongjie Chen",
"Yue Zhao",
"Franck Dernoncourt",
"Ryan A. Rossi",
"Hoda Eldardiry"
] | null | null | null | [] | null | 1 | null | null |
colm2026-0002 | CT-$\Delta$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models | null | [
"Kegeng Tang",
"Jingbo Wang",
"Shaogang Ren",
"Zihao WANG"
] | null | null | null | [] | null | 2 | null | null |
colm2026-0003 | No, there are no grammatical ``grandmother neurons'' in LLMs | null | [
"Linyang He",
"Nima Mesgarani"
] | null | null | null | [] | null | 3 | null | null |
colm2026-0004 | Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking | https://arxiv.org/abs/2604.01506 | [
"Zhanliang Wang",
"Hongzhuo Chen",
"Quan Minh Nguyen",
"Mian Umair Ahsan",
"Kai Wang"
] | null | null | Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference time. Existing post-hoc methods such as logit adjustment address this by adding a fixed classwise offset to the base-model logits. However, ... | [] | null | 4 | 2604.01506 | title_snapshot |
colm2026-0005 | Do LLMs Recognize Your Latent Preferences? A Benchmark for Latent Information Discovery in Personalized Interaction | https://arxiv.org/abs/2510.17132 | [
"Ioannis Tsaknakis",
"Bingqing Song",
"Shuyu Gan",
"Jiangweizhi Peng",
"Dongyeop Kang",
"Alfredo Garcia",
"Gaowen Liu",
"Charles Fleming",
"Mingyi Hong"
] | null | null | Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending restaurants or planning travel. In these scenarios, users rarely articulate every preference explicitly; instead, much of what they care about... | [] | null | 5 | 2510.17132 | title_snapshot |
colm2026-0006 | BibTeX Citation Hallucinations in Scientific Publishing Agents: Evaluation and Mitigation | https://arxiv.org/abs/2604.03159 | [
"Delip Rao",
"Chris Callison-Burch"
] | null | null | Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect current practice. We construct a benchmark of 931 papers across four scientifi... | [] | null | 6 | 2604.03159 | title_snapshot |
colm2026-0007 | KronQ: LLM Quantization via Kronecker-Factored Hessian | https://arxiv.org/abs/2607.07964 | [
"Donghyun Lee",
"Yuhang Li",
"Ruokai Yin",
"Priyadarshini Panda"
] | null | null | Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PTQ methods, including GPTQ, construct quantization objectives exclusively from input activation statistics, effectively assuming that all output channels contribute equa... | [] | null | 7 | 2607.07964 | title_snapshot |
colm2026-0008 | The Tool Illusion: Rethinking Tool Use in Web Agents | https://arxiv.org/abs/2604.03465 | [
"Renze Lou",
"Baolin Peng",
"Wenlin Yao",
"Qianhui Wu",
"Hao Cheng",
"Suman Nath",
"Wenpeng Yin",
"Jianfeng Gao"
] | null | null | As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool use as a higher-level action paradigm. Although prior studies have shown the promise of tools, their conclusions are often drawn from limited experimental scales and sometimes non-compara... | [] | null | 8 | 2604.03465 | title_snapshot |
colm2026-0009 | PreMoE: Proactive Inference for Efficient Mixture-of-Experts | https://arxiv.org/abs/2505.17639 | [
"Zehua Pei",
"Ying Zhang",
"Hui-Ling Zhen",
"Tao Yuan",
"Xianzhi Yu",
"Zhenhua Dong",
"Sinno Jialin Pan",
"Mingxuan Yuan",
"Bei Yu"
] | null | null | Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization. We introduce PreMoE, a training-free framework that proactively compiles sparse MoE variants for targeted deployment scenarios. At its core ... | [] | null | 9 | 2505.17639 | title_snapshot |
colm2026-0010 | Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR | https://arxiv.org/abs/2605.02909 | [
"Kazuki Egashira",
"Mark Vero",
"Jasper Dekoninck",
"Florian E. Dorner",
"Robin Staab",
"Martin Vechev"
] | null | null | Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designed for tasks with verifiable ground-truth answers, real-world verifiers (e.g., static code checkers) can introduce errors into the reward sig... | [] | null | 10 | 2605.02909 | title_snapshot |
colm2026-0011 | It’s How You Ask: Gendered Linguistic Bias in LLMs | null | [
"Katherine Van Koevering",
"Anjalie Field"
] | null | null | null | [] | null | 11 | null | null |
colm2026-0012 | ConsumerBench: Benchmarking Generative AI Applications on End-User Devices | https://arxiv.org/abs/2506.17538 | [
"Yile Gu",
"Rohan Kadekodi",
"Sahi Chitrapu",
"Aradhya Agrawal",
"Camille Sawa",
"Hoang Doan Nguyen",
"Keisuke Kamahori",
"Yiyu Liu",
"Baris Kasikci"
] | null | null | The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and user experience. This paper presents ConsumerBench, a comprehensive benchmarking framework designed to evaluate the system efficiency and respo... | [] | null | 12 | 2506.17538 | title_snapshot |
colm2026-0013 | RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs | https://arxiv.org/abs/2605.01913 | [
"Sadia Asif",
"Mohammad Mohammadi Amiri"
] | null | null | Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable to adversarial misuse. While prior work has shown that safety-relevant features are encoded in structured representations within the model's activation space, how these re... | [] | null | 13 | 2605.01913 | title_snapshot |
colm2026-0014 | Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces | https://arxiv.org/abs/2604.11996 | [
"Manas Pathak",
"Xingyao Chen",
"Shuozhe Li",
"Amy Zhang",
"Liu Leqi"
] | null | null | Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. This highlights a fundamental limitation of outcome-based evaluation: models may arrive at correct answers through f... | [] | null | 14 | 2604.11996 | title_snapshot |
colm2026-0015 | OpenStamp: A Watermark for Open-Source Language Models | null | [
"Miroojin Bakshi",
"Saksham Rastogi",
"Danish Pruthi"
] | null | null | null | [] | null | 15 | null | null |
colm2026-0016 | In-Context Examples Suppress Scientific Knowledge Recall in LLMs | https://arxiv.org/abs/2604.27540 | [
"Chaemin Jang",
"Woojin Park",
"Hyeok Yun",
"Dongman Lee",
"Jihee Kim"
] | null | null | Scientific reasoning rarely stops at what is directly observable; it often requires uncovering hidden structure from data. From estimating reaction constants in chemistry to inferring demand elasticities in economics, this latent structure recovery is what distinguishes scientific reasoning from curve fitting. Large la... | [] | null | 16 | 2604.27540 | title_snapshot |
colm2026-0017 | PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost | https://arxiv.org/abs/2603.21383 | [
"Junkeun Yi",
"Damon Mosk-Aoyama",
"Baihe Huang",
"Ritu Gala",
"Charles Wang",
"Sugam Devare",
"Khushi Bhardwaj",
"Abhibha Gupta",
"Oleksii Kuchaiev",
"Jiantao Jiao",
"Jian Zhang",
"Venkat Krishna Srinivasan"
] | null | null | Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers from out-of-domain (OOD) degradation. Conversely, end-to-end reinforcement learning (E2E RL) preserves OOD capabilities, but incurs high com... | [] | null | 17 | 2603.21383 | title_snapshot |
colm2026-0018 | Legibility is Not Interpretability: Evaluating Judged Importance versus Actual Importance in Chain-Of-Thought Reasoning Steps | null | [
"Kevin Du",
"Alexander Miserlis Hoyle",
"Laura Ruis",
"Acyr Locatelli"
] | null | null | null | [] | null | 18 | null | null |
colm2026-0019 | Learning Steerable Clarification Policies with Collaborative Self-play | https://arxiv.org/abs/2512.04068 | [
"Jonathan Berant",
"Maximillian Chen",
"Adam Fisch",
"Reza Aghajani",
"Fantine Eri Huot",
"Mirella Lapata",
"Jacob Eisenstein"
] | null | null | To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user intent and answer directly, (b) when to enumerate and answer multiple possible intents, and (c) when to ask a clarifying question. However, such policies are contextually dep... | [] | null | 19 | 2512.04068 | title_snapshot |
colm2026-0020 | Process-Oriented Evaluation of AI-Assisted Scientific Writing | https://arxiv.org/abs/2606.15583 | [
"Patrick Queiroz Da Silva",
"Sanchaita Hazra",
"Doeun Lee",
"Sachin Kumar",
"Bodhisattwa Prasad Majumder"
] | null | null | Bad writing hinders the publication of science. The role of artificial intelligence (AI) in generating and editing scientific texts remains unsettled. Abstracts serve as the critical gateway to scientific manuscripts, often shaping readers' interest. We inspect how individuals revise AI-generated abstracts compared to ... | [] | null | 20 | 2606.15583 | title_snapshot |
colm2026-0021 | Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty? | https://arxiv.org/abs/2605.12684 | [
"Yichen Feng",
"Yuetai Li",
"Chunjiang Liu",
"Fengqing Jiang",
"Yue Huang",
"Yuanyuan Chen",
"Hang Hua",
"Zhengqing Yuan",
"Kaiyuan Zheng",
"Luyao Niu",
"Bhaskar Ramasubramanian",
"Basel Alomair",
"Xiangliang Zhang",
"Misha Sra",
"Zichen Chen",
"Radha Poovendran",
"Zhangchen Xu"
] | null | null | Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a scalar score for a single image. We first ask whether ... | [] | null | 21 | 2605.12684 | title_snapshot |
colm2026-0022 | Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification | https://arxiv.org/abs/2601.16400 | [
"Zongwan Cao",
"Bingbing Wen",
"Lucy Lu Wang"
] | null | null | Real-world visual question answering (VQA) is often context-dependent: an image-question pair may be under-specified, such that the correct answer depends on external information that is not observable in the image. In such cases, directly answering can lead to confident but incorrect predictions. We propose CoA(Clarif... | [] | null | 22 | 2601.16400 | title_snapshot |
colm2026-0023 | MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs | https://arxiv.org/abs/2605.07305 | [
"Hsin-Ling Hsu",
"Zizheng Wang",
"Donghua Zhang",
"Nai-Chia Chen",
"Jerry Wang",
"Jun-En Ding",
"Chia-Hsuan Hsu",
"Guoan Wang",
"Feng Liu",
"Fang Ming Hung",
"Chenwei Wu",
"Liyue Shen"
] | null | null | Most existing LLM diagnoses are evaluated on static, single-turn settings where complete patient information is provided upfront, an oversimplification of real clinical practice. We study active diagnosis: the real-life clinical process of starting from initial observation, ordering tests, interpreting results, and upd... | [] | null | 23 | 2605.07305 | title_snapshot |
colm2026-0024 | When Verification Fails: How Compositionally Infeasible Claims Escape Rejection | https://arxiv.org/abs/2604.10990 | [
"Muxin Liu",
"Delip Rao",
"Grace Kim",
"Chris Callison-Burch"
] | null | null | Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries in evidence while preventing misinformation. This process involves evaluating each asserted constraint against validated evidence. Under the Closed-World Assumption (CWA)... | [] | null | 24 | 2604.10990 | title_snapshot |
colm2026-0025 | Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness | null | [
"Pardis Sadat Zahraei",
"Janvijay Singh",
"Gokhan Tur",
"Dilek Hakkani-Tür"
] | null | null | null | [] | null | 25 | null | null |
colm2026-0026 | SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA | https://arxiv.org/abs/2509.25459 | [
"Haozhou Xu",
"Dongxia Wu",
"Matteo Chinazzi",
"Ruijia Niu",
"Rose Yu",
"Yian Ma"
] | null | null | Large language models (LLMs) show promise in solving scientific problems. They can help generate long-form answers for scientific questions, which are crucial for comprehensive understanding of complex phenomena that require detailed explanations spanning multiple interconnected concepts and evidence. However, LLMs oft... | [] | null | 26 | 2509.25459 | title_snapshot |
colm2026-0027 | SAKE: Structured Agentic Knowledge Extrapolation for Complex LLM Reasoning via Reinforcement Learning | https://arxiv.org/abs/2505.15062 | [
"Jiashu He",
"Jinxuan Fan",
"Bowen Jiang",
"Ignacio Hounie",
"Dan Roth",
"Alejandro Ribeiro"
] | null | null | Knowledge extrapolation is the process of inferring novel information by combining and extending existing knowledge that is explicitly available. It is essential for solving complex questions in specialized domains where retrieving comprehensive external knowledge is impractical. We propose SAKE (Structured Agentic Kno... | [] | null | 27 | 2505.15062 | title_snapshot |
colm2026-0028 | FOCUS: Closed-Loop Attention Feedback for Efficient Vision-Language Understanding | null | [
"Akul Santhosh",
"Aastha Jhunjhunwala"
] | null | null | null | [] | null | 28 | null | null |
colm2026-0029 | Estimating near-verbatim extraction risk in language models with decoding-constrained beam search | https://arxiv.org/abs/2603.24917 | [
"A. Feder Cooper",
"Mark Lemley",
"Christopher De Sa",
"Lea Duesterwald",
"Allison Casasola",
"Jamie Hayes",
"Katherine Lee",
"Daniel E. Ho",
"Percy Liang"
] | null | null | Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -- computing the probability of generating a target suffix given a prefix under a decoding scheme -- addresses this, but is tractable only fo... | [] | null | 29 | 2603.24917 | title_snapshot |
colm2026-0030 | Subspace Control: Turning Constrained Model Steering into Controllable Spectral Optimization | https://arxiv.org/abs/2604.04231 | [
"Yancheng Huang",
"Changsheng Wang",
"Chongyu Fan",
"Yicheng Lang",
"Bingqi Shang",
"Yang Zhang",
"Mingyi Hong",
"Qing Qu",
"Alvaro Velasquez",
"Sijia Liu"
] | null | null | Foundation models, such as large language models (LLMs), are powerful but often require customization before deployment to satisfy practical constraints such as safety, privacy, and task-specific requirements, leading to "constrained" optimization problems for model steering and adaptation. However, solving such proble... | [] | null | 30 | 2604.04231 | title_snapshot |
colm2026-0031 | Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization | https://arxiv.org/abs/2510.16022 | [
"Changsheng Wang",
"Xin Chen",
"Sijia Liu",
"Ke Ding"
] | null | null | Adapting pretrained large language models (LLMs) to code domains via supervised fine-tuning (FT) has been commonly used for code generation. However, we identify a previously underappreciated failure mode, the memorization barrier, where strong memorization of downstream code data in the base model could trap optimizat... | [] | null | 31 | 2510.16022 | title_snapshot |
colm2026-0032 | Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations | https://arxiv.org/abs/2604.27093 | [
"Mingqian Zheng",
"Malia Morgan",
"Liwei Jiang",
"Carolyn Rose",
"Maarten Sap"
] | null | null | Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulness when benign users clarify their intent. We introduce CarryOnBench, the first interactive benchmark that measures whether LLMs can revise their interpretation of user in... | [] | null | 32 | 2604.27093 | title_snapshot |
colm2026-0033 | The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems? | https://arxiv.org/abs/2508.21143 | [
"Samrajnee Ghosh",
"Naman Agarwal",
"Ashish Goswami",
"Hemanshu Garg",
"Chinmay Mittal",
"Parag Singla",
"Mausam"
] | null | null | Cognitive science research treats visual perception, the ability to understand and make sense of a visual input, as one of the early developmental signs of intelligence. Its TVPS-4 framework categorizes and tests human perception into seven skills such as visual discrimination, and form constancy. Do Multimodal Large L... | [] | null | 33 | 2508.21143 | title_snapshot |
colm2026-0034 | OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data | https://arxiv.org/abs/2603.15594 | [
"Yuwen Du",
"Rui Ye",
"Shuo Tang",
"Xinyu Zhu",
"Yijun Lu",
"Yuzhu Cai",
"Siheng Chen"
] | null | null | Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains dominated by industrial giants due to a lack of transparent, high-quality training data. This persistent data scarcity has fundamentally hindered... | [] | null | 34 | 2603.15594 | title_snapshot |
colm2026-0035 | Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport | null | [
"Bohan Zhang",
"Anqi Ni",
"Yixin Wang",
"Paramveer Dhillon"
] | null | null | null | [] | null | 35 | null | null |
colm2026-0036 | Joint Optimization of Reasoning and Dual-Memory for Self-Learning Diagnostic Agent | https://arxiv.org/abs/2604.07269 | [
"Bingxuan Li",
"Simo Du",
"Yue Guo"
] | null | null | Clinical expertise improves not only by acquiring medical knowledge, but by accumulating experience that yields reusable diagnostic patterns. Recent LLMs-based diagnostic agents have shown promising progress in clinical reasoning for decision support. However, most approaches treat cases independently, limiting experie... | [] | null | 36 | 2604.07269 | title_snapshot |
colm2026-0037 | MoRFI: Monotonic Sparse Autoencoder Feature Identification | https://arxiv.org/abs/2604.26866 | [
"Dimitris Dimakopoulos",
"Shay B. Cohen",
"Ioannis Konstas"
] | null | null | Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduce new facts outwith the parametric knowledge, giving rise to hallucinations. While it has been demonstrated that supervised fine-tuning (SFT... | [] | null | 37 | 2604.26866 | title_snapshot |
colm2026-0038 | English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training | https://arxiv.org/abs/2604.13286 | [
"Mehak Preet Dhaliwal",
"Shashwat Ranjan Chaurasia",
"Yao Qin",
"Dezhi Hong",
"Thomas Butler"
] | null | null | Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities across languages. We present a systematic, controlled study of the interplay between training language coverage, model scale, and task domain, ba... | [] | null | 38 | 2604.13286 | title_snapshot |
colm2026-0039 | Scaling Self-Play with Self-Guidance | https://arxiv.org/abs/2604.20209 | [
"Luke Bailey",
"Kaiyue Wen",
"Kefan Dong",
"Tatsunori Hashimoto",
"Tengyu Ma"
] | null | null | LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together. However, in practice, existing LLM self-play methods do not scale well with large amounts of compute, instead hitting learning plateaus. We argue this ... | [] | null | 39 | 2604.20209 | title_snapshot |
colm2026-0040 | Procedural Knowledge at Scale Improves Reasoning | https://arxiv.org/abs/2604.01348 | [
"Di Wu",
"Devendra Singh Sachan",
"Wen-tau Yih",
"Mingda Chen"
] | null | null | Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat each problem in isolation and do not systematically reuse knowledge from prior reasoning trajectories. In particular, they underutilize procedural knowledge: how to reframe a... | [] | null | 40 | 2604.01348 | title_snapshot |
colm2026-0041 | Variational Co-Evolution via Reinforcement Learning | null | [
"Qiahao Zheng",
"Hancong Jin",
"Junbo Niu",
"Yinjie Wang",
"Wentao Zhang",
"Ruixun Zhang"
] | null | null | null | [] | null | 41 | null | null |
colm2026-0042 | Toward Scalable Terminal Task Synthesis via Skill Graphs | https://arxiv.org/abs/2604.25727 | [
"Zhiyuan Fan",
"TingHao YU",
"Yuanjun Cai",
"JiangTaoGuan",
"Yun Yang",
"Dingxin Hu",
"Xing W",
"Zhuo Han",
"feng zhang",
"Lilin Wang"
] | null | null | Terminal agents have demonstrated strong potential for autonomous command-line execution, yet their training remains constrained by the scarcity of high-quality and diverse execution trajectories. Existing approaches mitigate this bottleneck by synthesizing large-scale terminal task instances for trajectory sampling. H... | [] | null | 42 | 2604.25727 | title_snapshot |
colm2026-0043 | How Transformers Learn to Plan via Multi-Token Prediction | https://arxiv.org/abs/2604.11912 | [
"Jianhao Huang",
"Zhanpeng Zhou",
"Renqiu Xia",
"Baharan Mirzasoleiman",
"Weijie J Su",
"Wei Huang"
] | null | null | While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reasoning tasks. Multi-token prediction (MTP) has recently emerged as a promising alternative, yet its underlying mechanisms remain poorly understood. In this paper, we study... | [] | null | 43 | 2604.11912 | title_snapshot |
colm2026-0044 | Resa: Efficient Reasoning Models via SAEs | null | [
"Shangshang Wang",
"Julian Asilis",
"Ömer Faruk Akgül",
"Enes Burak Bilgin",
"Ollie Liu",
"Deqing Fu",
"Willie Neiswanger"
] | null | null | null | [] | null | 44 | null | null |
colm2026-0045 | Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis | https://arxiv.org/abs/2604.09874 | [
"Xinkai Zou",
"Yiming Huang",
"Zhuohang Wu",
"Jian Sha",
"Nan Huang",
"Longfei Yun",
"Jingbo Shang",
"Letian Peng"
] | null | null | Simulating how organized groups (e.g., corporations) make decisions (e.g., responding to a competitor's move) is essential for understanding real-world dynamics and could benefit relevant applications (e.g., market prediction). In this paper, we formalize this problem as a concrete research platform for group behavior ... | [] | null | 45 | 2604.09874 | title_snapshot |
colm2026-0046 | Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation | https://arxiv.org/abs/2604.03924 | [
"Xinyi Ling",
"Ye Liu",
"Reza Averly",
"Xia Ning"
] | null | null | Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns. Existing approaches address this challenge from different perspectives: structured methods enable multi-... | [] | null | 46 | 2604.03924 | title_snapshot |
colm2026-0047 | T-REX: Mixture-of-Rank-One-Experts with Semantic-aware Intuition for Multi-task Large Language Model Finetuning | https://arxiv.org/abs/2404.08985 | [
"Rongyu Zhang",
"Yijiang Liu",
"Huanrui Yang",
"Shenli Zheng",
"Li Du",
"Dan Wang",
"Yuan Du",
"Shanghang Zhang"
] | null | null | Large language models (LLMs) encounter significant adaptation challenges in diverse multitask finetuning. Mixture-of-experts (MoE) provides a promising solution with a dynamic architecture, enabling effective task decoupling. However, scaling up the number of MoE experts incurs substantial parameter and computational o... | [] | null | 47 | 2404.08985 | title_snapshot |
colm2026-0048 | Do LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations | https://arxiv.org/abs/2604.00209 | [
"Haoran Wang",
"Li Xiong",
"Kai Shu"
] | null | null | Large language models (LLMs) are increasingly deployed in high-stakes settings, yet they frequently violate contextual privacy by disclosing private information in situations where humans would exercise discretion. This raises a fundamental question: do LLMs internally encode contextual privacy norms, and if so, why do... | [] | null | 48 | 2604.00209 | title_snapshot |
colm2026-0049 | Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring | null | [
"Matthew Bramwell Bone",
"Fabian Stephany",
"R. Maria del Rio-Chanona"
] | null | null | null | [] | null | 49 | null | null |
colm2026-0050 | Safety Cost of Steering Vectors Is Separable and Reducible | null | [
"Yuxiao Li",
"Gjergji Kasneci"
] | null | null | null | [] | null | 50 | null | null |
colm2026-0051 | Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions | https://arxiv.org/abs/2602.05220 | [
"Jinchuan Tian",
"Haoran Wang",
"Bo-Hao Su",
"Chien-yu Huang",
"Qingzheng Wang",
"Jiatong Shi",
"William Chen",
"Xun Gong",
"Siddhant Arora",
"Chin-Jou Li",
"Masao Someki",
"Takashi Maekaku",
"Yusuke Shinohara",
"Jin Sakuma",
"Keita Goto",
"Chao-Han Huck Yang",
"Shinji Watanabe"
] | null | null | Current audio foundation models typically rely on rigid, task-specific supervision, addressing isolated factors of audio rather than the whole. In contrast, human intelligence processes audio holistically, seamlessly bridging physical signals with abstract cognitive concepts to execute complex tasks. Grounded in this p... | [] | null | 51 | 2602.05220 | title_snapshot |
colm2026-0052 | Introspective Diffusion Language Models | https://arxiv.org/abs/2604.11035 | [
"Yifan Yu",
"Yuqing Jian",
"Junxiong Wang",
"Zhongzhu Zhou",
"Donglin Zhuang",
"Xinyu Fang",
"Xiaoxia Wu",
"Qingyang Wu",
"Shuaiwen Leon Song",
"Tri Dao",
"Ben Athiwaratkun",
"James Zou",
"Fan Lai",
"Chenfeng Xu"
] | null | null | Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of introspective consistency: AR models agree with their own generations, while DLMs often do not. We define the introspective acceptance rate, which measures whether a model ... | [] | null | 52 | 2604.11035 | title_snapshot |
colm2026-0053 | CoreSemDB: Benchmarking Hybrid Semantic-Relational Query Processing over Text-Rich Databases | null | [
"Yuchen Tian",
"Jianxiong Guo",
"Hao Zhang",
"Jing Ma",
"Congli Gao"
] | null | null | null | [] | null | 53 | null | null |
colm2026-0054 | A Self-Pruning Transformer: Extreme KV-Cache Compression with Universal Attention | null | [
"Davis Wertheimer",
"Haochen Shen",
"Ahan Gupta",
"Derrick Liu",
"Yu Chin Fabian Lim",
"Mudhakar Srivatsa",
"Raghu K. Ganti",
"Minjia Zhang",
"Naigang Wang"
] | null | null | null | [] | null | 54 | null | null |
colm2026-0055 | Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning? | null | [
"Arnav Hiray",
"Agam Shah",
"Caleb Lu",
"Meghaj Tarte",
"Harsit Mittal",
"Sudheer Chava"
] | null | null | null | [] | null | 55 | null | null |
colm2026-0056 | Learning Next Action Predictors from Human-Computer Interaction | https://arxiv.org/abs/2603.05923 | [
"Omar Shaikh",
"Valentin Teutschbein",
"Kanishk Gandhi",
"Yikun Chi",
"Nick Haber",
"Thomas N. Robinson",
"Nilam Ram",
"Byron Reeves",
"Sherry Yang",
"Michael S. Bernstein",
"Diyi Yang"
] | null | null | Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire context of what we see and do. We formalize this as next action prediction (NAP): given a sequence of a user's multimoda... | [] | null | 56 | 2603.05923 | title_snapshot |
colm2026-0057 | Human vs Machine Translation Detection: A Cross-Model, Cross-Domain, and Low-Resource Analysis | null | [
"Yabsera Yemanberhan",
"Prasenjit Mitra",
"Roald Eiselen"
] | null | null | null | [] | null | 57 | null | null |
colm2026-0058 | Generating Pretraining Tokens from Organic Data for Data-Bound Scaling | https://arxiv.org/abs/2605.17849 | [
"Zichun Yu",
"Chenyan Xiong"
] | null | null | LLM pretraining is shifting from a compute-bound to a data-bound regime, where available human (organic) text falls far short of scaling demands. However, reaching the data-bound regime does not mean the model has fully utilized its organic corpus. In this paper, we introduce SynPro, a synthetic data generation framewo... | [] | null | 58 | 2605.17849 | title_snapshot |
colm2026-0059 | Latent Structure of Affective Representations in Large Language Models | https://arxiv.org/abs/2604.07382 | [
"Benjamin J. Choi",
"Melanie Weber"
] | null | null | The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implications for model transparency and AI safety. Existing literature has focused mainly on general geometric and topological properties of the learnt representations, but due to a lac... | [] | null | 59 | 2604.07382 | title_snapshot |
colm2026-0060 | CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes | null | [
"Yufan Wu",
"Zhengyi Hu",
"Lang Wei",
"Ruichen Li",
"Qifan Yang",
"Yinghui He",
"Ting Zhu"
] | null | null | null | [] | null | 60 | null | null |
colm2026-0061 | Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving | null | [
"Songyang Gao",
"Yuzhe Gu",
"Zijian Wu",
"Lingkai Kong",
"Wenwei Zhang",
"ZhongruiCai",
"Fan Zheng",
"Tianyou Ma",
"Junhao Shen",
"Haiteng Zhao",
"Duanyang Zhang",
"Huilun Zhang",
"Kuikun Liu",
"Chengqi Lyu",
"YanhuiDuan",
"Chiyu Chen",
"Ningsheng Ma",
"Jianfei Gao",
"Han Lyu",
... | null | null | null | [] | null | 61 | null | null |
colm2026-0062 | Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models | https://arxiv.org/abs/2603.20957 | [
"Xinyue Liu",
"Niloofar Mireshghallah",
"Jane C. Ginsburg",
"Tuhin Chakrabarty"
] | null | null | Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on safety alignment strategies via RLHF, system prompts, and output filters to block verbatim regurgitation of copyrighted works, and have cited the efficacy of these measures i... | [] | null | 62 | 2603.20957 | title_snapshot |
colm2026-0063 | Blind Refusal: Language Models Refuse to Help Users Evade Unjust, Absurd, and Illegitimate Rules | https://arxiv.org/abs/2604.06233 | [
"Cameron Pattison",
"Lorenzo Manuali",
"Seth Lazar"
] | null | null | Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help evading rules imposed by an illegitimate authority, rules that are deeply unjust or absurd in their content or application, or rules that admit of justified exceptions, re... | [] | null | 63 | 2604.06233 | title_snapshot |
colm2026-0064 | Learning to Learn from Language Feedback with Social Meta-Learning | https://arxiv.org/abs/2602.16488 | [
"Jonathan Cook",
"Diego Antognini",
"Martin Klissarov",
"Claudiu Cristian Musat",
"Edward Grefenstette"
] | null | null | Large language models (LLMs) often struggle to learn from corrective feedback within a conversational context. They are rarely proactive in soliciting this feedback, even when faced with ambiguity, which can make their dialogues feel static, one-sided, and lacking the adaptive qualities of human conversation. To addres... | [] | null | 64 | 2602.16488 | title_snapshot |
colm2026-0065 | Instruction-tuned LLMs for Full Argumentative Component Detection | null | [
"Sofiane Elguendouze",
"Erwan Hain",
"Elena Cabrio",
"Serena Villata"
] | null | null | null | [] | null | 65 | null | null |
colm2026-0066 | YC-Bench: Benchmarking AI Agents for Long-Term Planning and Consistent Execution. | https://arxiv.org/abs/2604.01212 | [
"Muyu He",
"Adit Jain",
"Anand Kumar",
"Vincent Tu",
"Soumyadeep Bakshi",
"Sachin Patro",
"Nazneen Rajani"
] | null | null | As LLM agents tackle increasingly complex tasks, a critical question is whether they can maintain strategic coherence over long horizons: planning under uncertainty, learning from delayed feedback, and adapting when early mistakes compound. We introduce $\texttt{YC-Bench}$, a benchmark that evaluates these capabilities... | [] | null | 66 | 2604.01212 | title_snapshot |
colm2026-0067 | TritonRL: Training LLMs to Think and Code Triton Without Cheating | https://arxiv.org/abs/2510.17891 | [
"Jiin Woo",
"Shaowei Zhu",
"Allen Nie",
"Zhen Jia",
"Yida Wang",
"Youngsuk Park"
] | null | null | The rapid evolution of Large Language Models (LLMs) has driven a growing demand for automated, high-performance system kernels to accelerate machine learning workloads. We introduce TritonRL, a domain-specialized 8B-scale LLM for Triton programming, trained via a novel reinforcement learning (RL) framework. While Trito... | [] | null | 67 | 2510.17891 | title_snapshot |
colm2026-0068 | Emergent Negligence: How Profit Mandates Induce Alignment Failures in LLMs | null | [
"Eric So"
] | null | null | null | [] | null | 68 | null | null |
colm2026-0069 | Language Models Might Not Understand You: Evaluating Theory of Mind via Story Prompting | https://arxiv.org/abs/2506.19089 | [
"Nathaniel Getachew",
"Abulhair Saparov"
] | null | null | We introduce StorySim, a programmable framework for synthetically generating stories to evaluate the theory of mind (ToM) and world modeling (WM) capabilities of large language models (LLMs). Unlike prior benchmarks that may suffer from contamination in pretraining data, or rely on an LLM for generation, StorySim produ... | [] | null | 69 | 2506.19089 | title_snapshot |
colm2026-0070 | Routing Entropy: A Hidden Self-Verifier for Free in Mixture-of-Experts LLMs | null | [
"Zhongyang Li",
"Ziyue Li",
"Tianyi Zhou"
] | null | null | null | [] | null | 70 | null | null |
colm2026-0071 | Discovering Hierarchical Latent Capabilities of Language Models from Observational Evaluation Data | null | [
"Jikai Jin",
"Vasilis Syrgkanis",
"Sham M. Kakade",
"Hanlin Zhang"
] | null | null | null | [] | null | 71 | null | null |
colm2026-0072 | TierMem: Balancing Compressed Memory and Raw Evidence for Long-Horizon Agent Memory | null | [
"Qiming Zhu",
"Shunian Chen",
"Rui Yu",
"Zhehao Wu",
"Benyou Wang"
] | null | null | null | [] | null | 72 | null | null |
colm2026-0073 | Extended to Reality: Prompt Injection in 3D Environments | https://arxiv.org/abs/2602.07104 | [
"Zhuoheng Li",
"Ying Chen"
] | null | null | Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering diverse applications such as robotics and situated conversational agents. When MLLMs reason over camera-captured views of the physical world, a new attack surface emerges: an attac... | [] | null | 73 | 2602.07104 | title_snapshot |
colm2026-0074 | WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild | https://arxiv.org/abs/2605.01018 | [
"Junzhe Huang",
"Xiaoxiao Sun",
"Yan Yang",
"Yuxuan Hou",
"Ruotian Zhang",
"Sirui Li",
"Hehe Fan",
"Serena Yeung-Levy",
"Xin Yu"
] | null | null | Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of in-the-wild table images underexplore... | [] | null | 74 | 2605.01018 | title_snapshot |
colm2026-0075 | SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation | https://arxiv.org/abs/2603.08910 | [
"Hexuan Wang",
"Yaxuan Ren",
"Srikar Bommireddypalli",
"Shuxian Chen",
"Adarsh Prabhudesai",
"Rongkun Zhou",
"Elina Baral",
"Philipp Koehn"
] | null | null | We introduce SciTaRC, an expert-authored benchmark of questions about tabular data in scientific papers requiring both deep language reasoning and complex computation. We show that current state-of-the-art AI models fail on at least 23% of these questions, a gap that remains significant even for highly capable open-wei... | [] | null | 75 | 2603.08910 | title_snapshot |
colm2026-0076 | Semantic Differentiation for Tackling Challenges in Watermarking Low-Entropy Constrained Generation Outputs | https://arxiv.org/abs/2601.11629 | [
"Nghia T. Le",
"Alan Ritter",
"Kartik Goyal"
] | null | null | We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate at watermarking LM outputs for constrained generation tasks with low-entropy output spaces. Therefore, we devise SeqMark, a sequence-level watermarking algorithm with semantic di... | [] | null | 76 | 2601.11629 | title_snapshot |
colm2026-0077 | When Do LLMs Admit Their Mistakes? Understanding The Role Of Model Belief In Retraction | https://arxiv.org/abs/2505.16170 | [
"Yuqing Yang",
"Robin Jia"
] | null | null | Can large language models (LLMs) admit their mistakes when they should know better? In this work, we study when and why LLMs choose to retract, i.e., spontaneously and immediately acknowledge their errors. Using model-specific testbeds, we find that while LLMs are capable of retraction, they do so only rarely, even whe... | [] | null | 77 | 2505.16170 | title_snapshot |
colm2026-0078 | Barriers to Universal Reasoning with Transformers (and How to Overcome them) | https://arxiv.org/abs/2604.25800 | [
"Oliver Kraus",
"Yash Sarrof",
"Yuekun Yao",
"Alexander Koller",
"Michael Hahn"
] | null | null | Chain-of-Thought (CoT) has been shown to empirically improve Transformers' performance, and theoretically increase their expressivity to Turing completeness. However, whether Transformers can learn to generalize to CoT traces longer than those seen during training is understudied. We use recent theoretical frameworks f... | [] | null | 78 | 2604.25800 | title_snapshot |
colm2026-0079 | Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Length | https://arxiv.org/abs/2506.08184 | [
"Chupei Wang",
"Jiaqiu Vince Sun"
] | null | null | Information retrieval in Large Language Models (LLMs) is increasingly recognized as intertwined with generation capabilities rather than mere lookup. While longer contexts are often assumed to improve retrieval, the effects of intra-context interference remain understudied. To address this, we adapt the proactive inter... | [] | null | 79 | 2506.08184 | title_snapshot |
colm2026-0080 | Restoring Generalization in Fine-tuned Multimodal LLMs via Geometric Alignment | null | [
"Lixuan He",
"Shikang Zheng",
"Guantao Chen"
] | null | null | null | [] | null | 80 | null | null |
colm2026-0081 | SWE-chat: Coding Agent Interactions From Real Users in the Wild | https://arxiv.org/abs/2604.20779 | [
"Joachim Baumann",
"Vishakh Padmakumar",
"Xiang Li",
"John Yang",
"Diyi Yang",
"Sanmi Koyejo"
] | null | null | AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat, the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild. The dataset currently contai... | [] | null | 81 | 2604.20779 | title_snapshot |
colm2026-0082 | VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents | https://arxiv.org/abs/2603.23840 | [
"Yuhao Chen",
"Yi Xu",
"Xinyun Ding",
"Xiang Fang",
"Shuochen Liu",
"Luxi Lin",
"Qingyu Zhang",
"Ya Li",
"Quan Liu",
"Tong Xu"
] | null | null | With the growing demand for intelligent in-vehicle experiences, vehicle-based agents are evolving from simple assistants to long-term companions. This evolution requires agents to continuously model multi-user preferences and make reliable decisions in the face of inter-user preference conflicts and changing habits ove... | [] | null | 82 | 2603.23840 | title_snapshot |
colm2026-0083 | ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability | https://arxiv.org/abs/2510.09062 | [
"Chung-En Sun",
"Ge Yan",
"Akshay R. Kulkarni",
"Tsui-Wei Weng"
] | null | null | Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects critical to trustworthiness. We argue that usable reasoning systems must be trustworthy, characterized by three properties: interpretability, faithfulness, and reliability. T... | [] | null | 83 | 2510.09062 | title_snapshot |
colm2026-0084 | The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior | null | [
"Xiangzhe Xu",
"Hamidreza Saghir",
"Qianhui Wu",
"Marc-Alexandre Côté",
"Tong Wang",
"Kiran Lakkaraju",
"Kexin Pei",
"Xiangyu Zhang"
] | null | null | null | [] | null | 84 | null | null |
colm2026-0085 | Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges | https://arxiv.org/abs/2602.13576 | [
"Ruomeng Ding",
"Yifei Pang",
"He Sun",
"Yizhong Wang",
"Steven Wu",
"Zhun Deng"
] | null | null | Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-language rubrics and validated on benchmarks. We identify a previously under-recognized vulnerability in this workflow, which we term Rubric-Induced Preference Drift (RIPD). Even when ... | [] | null | 85 | 2602.13576 | title_snapshot |
colm2026-0086 | DeepScholar-Bench: A Live Benchmark for Automated Evaluation of Generative Research Synthesis | null | [
"Liana Patel",
"Negar Arabzadeh",
"Harshit Gupta",
"Ankita Sundar",
"Alon Y. Halevy",
"Ion Stoica",
"Matei Zaharia",
"Carlos Guestrin"
] | null | null | null | [] | null | 86 | null | null |
colm2026-0087 | ADAG: Automatically Describing Attribution Graphs | https://arxiv.org/abs/2604.07615 | [
"Aryaman Arora",
"Zhengxuan Wu",
"Jacob Steinhardt",
"Sarah Schwettmann"
] | null | null | In language model interpretability research, \textbf{circuit tracing} aims to identify which internal features causally contributed to a particular output and how they affected each other, with the goal of explaining the computations underlying some behaviour. However, all prior circuit tracing work has relied on ad-ho... | [] | null | 87 | 2604.07615 | title_snapshot |
colm2026-0088 | Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search | https://arxiv.org/abs/2510.18939 | [
"Howard Yen",
"Yoonsang Lee",
"Ashwin Paranjape",
"Mengzhou Xia",
"Thejas Venkatesh",
"Jack Hessel",
"Danqi Chen",
"Yuhao Zhang"
] | null | null | Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling powerful applications like deep research systems. In this work, we show that popular agentic search frameworks struggle to scale to long trajectories primarily due to cont... | [] | null | 88 | 2510.18939 | title_snapshot |
colm2026-0089 | VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean | https://arxiv.org/abs/2602.18307 | [
"Yutong Xin",
"Jocelyn Qiaochu Chen",
"Greg Durrett",
"Isil Dillig"
] | null | null | Large language models have achieved striking results in interactive theorem proving, particularly in Lean. However, most benchmarks for LLM-based proof automation are drawn from mathematics in the Mathlib ecosystem, whereas proofs in software verification are developed inside definition-rich codebases with substantial ... | [] | null | 89 | 2602.18307 | title_snapshot |
colm2026-0090 | PolicyLong: Towards On-Policy Context Extension | https://arxiv.org/abs/2604.07809 | [
"Junlong Jia",
"Ziyang Chen",
"Xing W",
"Chaochen Gao",
"TingHao YU",
"feng zhang",
"Songlin Hu",
"Binghui Guo"
] | null | null | Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencies via information-theoretic verification, selecting contexts that reduce a base model's predictive entropy. However, their single-pass offline construction with a fixed m... | [] | null | 90 | 2604.07809 | title_snapshot |
colm2026-0091 | Structure Before Collapse: Transient Semantic Geometry in Next-Token Prediction | https://arxiv.org/abs/2606.26749 | [
"Yize Zhao",
"Isabel Papadimitriou",
"Christos Thrampoulidis"
] | null | null | Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the output label and ignores any semantic similarity in the inputs. This creates a puzzle: next-token prediction language models are trained predom... | [] | null | 91 | 2606.26749 | title_snapshot |
colm2026-0092 | MetaLint: Easy-to-Hard Generalization for Code Linting | https://arxiv.org/abs/2507.11687 | [
"Atharva Naik",
"Lawanya Baghel",
"Dhatchinamoorthi Kunde Govindarajan",
"Darsh Agrawal",
"Yiqing Xie",
"Daniel Fried",
"Carolyn Rose"
] | null | null | Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during training. We introduce MetaLint, a meta-learning framework that formulates code linting as an instruction-following task, where a model evaluates ... | [] | null | 92 | 2507.11687 | title_snapshot |
colm2026-0093 | Path-Constrained Mixture-of-Experts | https://arxiv.org/abs/2603.18297 | [
"Zijin Gu",
"Tatiana Likhomanenko",
"Vimal Thilak",
"Jason Ramapuram",
"Navdeep Jaitly"
] | null | null | Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph{expert paths} -- the sequence of expert selections a token makes across all layers. This perspective reveals that, despite $N^L$ possible p... | [] | null | 93 | 2603.18297 | title_snapshot |
colm2026-0094 | Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models | https://arxiv.org/abs/2503.17349 | [
"Jianing Qi",
"Jiawei Liu",
"Hao Tang",
"Zhigang Zhu"
] | null | null | Vision Language Models (VLMs) excel at identifying and describing objects but often fail at spatial reasoning. We study why VLMs, such as LLaVA, underutilize spatial cues despite having positional encodings and spatially rich vision encoder features. Our analysis reveals a key imbalance: vision token embeddings have mu... | [] | null | 94 | 2503.17349 | title_snapshot |
colm2026-0095 | What AI Benchmarks Actually Measure: Adapting Convergent and Discriminant Validity to Interrogate Fifty-Six AI Benchmarks | null | [
"Meera Desai",
"Sang T. Truong",
"Hanna Wallach",
"Alex Chouldechova",
"A. Feder Cooper",
"Jean Garcia-Gathright",
"Daniel E. Ho",
"Abigail Z. Jacobs",
"Sanmi Koyejo",
"Nicholas Pangakis",
"Angelina Wang"
] | null | null | null | [] | null | 95 | null | null |
colm2026-0096 | Understanding Machine Unlearning Through the Lens of Mode Connectivity | https://arxiv.org/abs/2504.06407 | [
"Jiali Cheng",
"Hadi Amiri"
] | null | null | Machine Unlearning aims to remove undesired information from trained models
without requiring full retraining from scratch. Despite recent advancements,
their underlying loss landscapes and optimization dynamics received less
attention. In this paper, we investigate and analyze machine unlearning through
the lens of ... | [] | null | 96 | 2504.06407 | title_snapshot |
colm2026-0097 | Off-Policy Token-Level Labels Improve Out-of-Distribution Generalization for Summarization | null | [
"Zitong Huang",
"Gustavo Adolpho Lucas de Carvalho",
"Deqing Fu",
"Robin Jia"
] | null | null | null | [] | null | 97 | null | null |
colm2026-0098 | Neuro-symbolic PRM: Enhancing Scientific Reasoning via Structured Traces and Symbolic Verification | null | [
"Yuxin Zi",
"Cong Xu",
"Suparna Bhattacharya",
"Martin Foltin",
"Amit Sheth"
] | null | null | null | [] | null | 98 | null | null |
colm2026-0099 | TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law | https://arxiv.org/abs/2507.21134 | [
"Zheng Hui",
"Yijiang River Dong",
"Ehsan Shareghi",
"Nigel Collier"
] | null | null | As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evalua... | [] | null | 99 | 2507.21134 | title_snapshot |
colm2026-0100 | Studying the Soupability of Documents in State Space Models | https://arxiv.org/abs/2505.24033 | [
"Yasaman Jafari",
"Zixian Wang",
"Leon Bergen",
"Taylor Berg-Kirkpatrick"
] | null | null | We investigate whether hidden states from Structured State Space Models (SSMs) can be merged post hoc to support downstream reasoning. Inspired by model souping, we study document souping, a strategy where documents are encoded independently, and their representations are pooled, via simple operations like averaging, i... | [] | null | 100 | 2505.24033 | title_snapshot |
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