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Open access
Aug 2026
Towards Trustworthy Large Language Models
An integrated conceptual frame-work that couples attention- and perturbation-based explainability with lightweight hallucination-detection signals and token-efficient inference strategies is presented, and a set of cross-cutting consistency metrics are instrumented with a set of cross-cutting consistency metrics.
Sakshi Parate, Shreyans Sanyal
· Advanced International Journ... · 0 citations
Preprint
Aug 2026
Encoded but Not Actionable: Auditing the Decode-Generate-Steer Gap in Frozen LLMs for Geometric Constraints
Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model behavior remain unclear. We investigate this question through geometric reasoning, using parametric CAD constraints as a controlled testbed for separating local pairwise rel...
M. Liang, Xin-Zhao Cheng, Faizan Wajid
· 0 citations
Jul 2026
Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models
Activation steering controls model behavior by editing internal activations at inference time, and finds that a single MLP neuron is eval-correlated but not causal at both scales, and that scanning the real Pile yields a natural-text baseline competitive with the optimizer for the internal direction.
Deepanshu Mody, Samar Agarwal, Utkarsh Mittal et al.
· arXiv.org · 0 citations
Jul 2026
Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning
Switch-Reasoner is proposed, a GRPO-based framework that learns to adaptively select reasoning modes for MLLMs and introduces a dual-level regulation mechanism that balances the overall use of Thinking Mode and Direct Mode while providing sample-level supervision based on the relative benefit of the two choices.
Yiyang Fang, Pei Fu, Jinjie Li et al.
· arXiv.org · 0 citations
Preprint
Aug 2026
Recognition-Conditioned Reasoning: A Training-Free Multimodal-LLM Pipeline for Fine-Grained Micro-Action Understanding
This work presents the training-free, prompt-only system that won first place in the fine-grained understanding track (MA-Bench) of the MAC~2026 Micro-Action Challenge, where both fine-tuning and ground-truth supervision are disallowed.
Fengshun Wang, Jin'ang Han, Zhigang Tu
· 1 citation
Preprint
Aug 2026
Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique
The Latent Critic is introduced, a lightweight low-rank adapter that operates concurrently with a frozen base LLM's generation to actively restructure the transformer's residual stream---amplifying latent grounding signals and translating them into localized, natural language feedback within a single sequence.
S. Vijayvargiya, R. Lokesh
· 2 citations