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Experiments with representative closed-source and open-source MLLMs show that OCR-grounded meta-reasoning remains far from saturated: models struggle with visible-rule application and layout-sensitive inference, while process-compliant rationales can accompany incorrect final answers under exact-match evaluation.
By eliminating routers entirely, CD-LoRA employs a consistency-driven alignment mechanism to enforce representation congruence across tasks in a shared low-rank space, which fosters robust, task-agnostic features without explicit partitioning overhead.
CaSKG, a counterfactual-causal skill graph framework that calibrates procedural relations before retrieval, is proposed, position edge-confidence calibration as an effective route to compact and executable skill retrieval at scale.
This work applies Top-K Sparse Autoencoders to the intermediate representations of DeepSeek-R1-Distill-Qwen-7B and examines the model's divergent behaviors across math-solving tasks of three distinct difficulty levels, identifying a clear distinction in how the model functions under two reasoning modes.
ToolRobustBench provides a deterministic and cascade-aware benchmark for diagnosing robustness beyond clean tool-calling accuracy, where a tool-calling agent is an LLM system that selects a tool, supplies structured arguments, and interprets its returned feedback.
A D-LLM hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short) that outperforms existing hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.
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