arXiv AI

Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models

arXiv:2608. 14252v1 Announce Type: new Abstract: Recent work suggests that some large language model representations have content or reference.

arXiv Machine Learning

Aug 4

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

arXiv:2507. 02778v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have transformed AI, they still make errors and follow unproductive reasoning paths.

By Ken Tsui

llms fine-tuning safety

arXiv Machine Learning

Jun 5

Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents

arXiv:2606. 05263v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chains, belief drift, and shortcut actions that satisfy terminal checks.

By Renwei Meng

agents reinforcement-learning

arXiv AI

Jun 3

Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling

arXiv:2606. 02837v1 Announce Type: cross Abstract: Accurate translation from Natural Language to First-Order Logic (NL-to-FOL) underpins neurosymbolic AI systems and Natural Language Inference (NLI), making the quality of NL-to-FOL benchmarks essential -- yet these datasets have never been rigorously audited.

By Andrea Brunello, Cristian Curaba, Luca Geatti, Michele Mignani, Angelo Montanari, Nicola Saccomanno

llms benchmarks

arXiv AI

Jul 31

A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models

arXiv:2607. 26102v1 Announce Type: cross Abstract: Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference.

By Vivek Shukla, Varun Shukla, Atul, Divya Mishra, Mehul Kumar Das

llms

arXiv Machine Learning

Aug 5

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.

By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y

llms nlp benchmarks safety

arXiv AI

Aug 6

CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models

arXiv:2608. 04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer.

By De Jiang, Zhengyang Zhang, Kehong Yuan, Shaohua Ma

llms multimodal safety

arXiv AI

Jun 10

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509. 25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.

By Zhepei Wei, Xiao Yang, Kai Sun, Jiaqi Wang, Rulin Shao, Jingxiang Chen, Mohammad Kachuee, Teja Gollapudi, Yiwei Liao, Nicolas Scheffer, Rakesh Wanga, Anuj Kumar, Yu Meng, Wen-tau Yih, Xin Luna Dong

llms nlp reinforcement-learning benchmarks safety

arXiv AI

Jul 3

Epistemic Goggles: A Pretrained Module that Induces an Epistemic Frame via Gradient Editing

arXiv:2607. 01690v1 Announce Type: new Abstract: Finetuning a language model on documents that are explicitly annotated as fictional results in a model that still actually believes the documents' core claims, an effect known as Negation Neglect.

By Joshua Penman

llms fine-tuning safety

arXiv AI

Jul 31

Adversarial Pragmatics for AI Safety Evaluation: A Diagnostic Framework and Seed Benchmark for Language-Mediated Control

arXiv:2607. 01153v3 Announce Type: replace-cross Abstract: Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model followed an instruction, refused appropriately, complied with a policy, or misreported progress in an agentic task.

By Brett Reynolds

llms agents benchmarks safety

arXiv Machine Learning

Aug 10

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

arXiv:2608. 06417v1 Announce Type: new Abstract: The proliferation of misinformation online has driven demand for scalable detection systems.

By Pedro Barcelos, Ot\'avio Parraga, Marcelo M. Mussi, Lucas M. Fraga, Lucas S. Kupssinsk\"u, Rodrigo C. Barros

llms diffusion fine-tuning benchmarks safety

arXiv AI

1d ago

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR

arXiv:2605. 02909v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs).

By Kazuki Egashira, Mark Vero, Jasper Dekoninck, Florian E. Dorner, Robin Staab, Martin Vechev

llms reinforcement-learning

arXiv AI

Jul 22

Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning

arXiv:2607. 19345v1 Announce Type: cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier.

By Lizhe Fang, Weizhou Shen, Tianyi Tang, Yisen Wang

llms reinforcement-learning benchmarks

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