arXiv Machine Learning

Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations

arXiv:2606. 15779v1 Announce Type: cross Abstract: Multimodal models can name the action units (AUs) behind a facial emotion, but their AU->emotion rationales are typically plausible rather than faithful: nothing forces the AUs a model invokes to be the AUs that actually drive its prediction.

Hugging Face Trending Papers

Jul 14

Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLMs

Aligned language models routinely misreport under non-evidential incentive pressure: they agree with a confident user or overstate certainty even when their internal belief is unchanged. We cast this as a failure of internal incentive-compatibility (IC) and present a method for learning and certifying counterfactual report mediators that hold a model's reports to a causal contract: invariant to forbidden influences (pressure, prestige, restyling) and responsive to licensed ones (genuine evidence).

llms fine-tuning benchmarks

arXiv AI

Jul 15

Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLMs

arXiv:2607. 12985v1 Announce Type: new Abstract: Aligned language models routinely misreport under non-evidential incentive pressure: they agree with a confident user or overstate certainty even when their internal belief is unchanged.

By Sen Yang, Yuen-Hei Yeung

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Evidence-Type Competition: When Can Interventional Data Teach Language Models Causal Direction?

arXiv:2607. 29484v1 Announce Type: cross Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning.

By Xining Xun

llms robotics

arXiv Machine Learning

Aug 10

NTDH: Complex Reasoning for Comprehensive Affective Analysis

arXiv:2608. 06425v1 Announce Type: cross Abstract: Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs, and affective meaning is context-dependent, requiring conflicting cues to be reconciled rather than mapped directly to labels.

By Tianlei Zhu, Zhiwei Liu, Yuyan Wang, Xiao-Yang Liu, Sophia Ananiadou

fine-tuning benchmarks

arXiv AI

Jul 2

Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations

arXiv:2503. 13445v3 Announce Type: replace-cross Abstract: When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.

By Noah Y. Siegel, Nicolas Heess, Maria Perez-Ortiz, Oana-Maria Camburu

llms

arXiv AI

Aug 3

On the Generalization of Steering Vectors for Chain-of-Thought Faithfulness

arXiv:2607. 29062v1 Announce Type: new Abstract: Model capabilities have improved in large part due to scaling chain of thought.

By Matthew Nguyen, Kyle Cox, Austin Meek, Iv\'an Arcuschin

safety

arXiv AI

Aug 10

Counterfactual Simulation Training for Chain-of-Thought Faithfulness

arXiv:2602. 20710v2 Announce Type: replace Abstract: Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output.

By Peter Hase, Christopher Potts

llms reinforcement-learning

arXiv AI

Jul 7

Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning

arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.

By Matthew Foutter, Matteo Cercola, Lena Wild, Yunshan Wang, Michelle Li, Daniele Gammelli, Marco Pavone

agents reinforcement-learning robotics multimodal benchmarks safety

arXiv Machine Learning

Jun 5

Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models

arXiv:2606. 05378v1 Announce Type: new Abstract: We test whether a single screen-and-ablate recipe -- identify attention-head circuits by task-pattern selectivity, then verify by causal ablation against a matched-random null -- produces consistent mechanistic claims across model families.

By Yongzhong Xu

llms benchmarks

arXiv Machine Learning

Jun 5

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR

arXiv:2606. 05932v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) improves reasoning even when the reward signal is spurious -- assigning credit to the group-plurality answer rather than a ground-truth verifier.

By Yuze Gao

reinforcement-learning safety

arXiv Machine Learning

Jun 30

Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring

arXiv:2606. 30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated.

By Max Fomin, Elad David, Amit LeVi

llms agents fine-tuning

arXiv AI

Jun 2

Causal state binding predicts action control in language agents

arXiv:2605. 09692v3 Announce Type: replace Abstract: Autonomous language agents increasingly expose traces, memories, plans and constraints, but existing evaluations rarely test whether these state variables are bound to final actions.

By Xiao Jia

llms agents benchmarks

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