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Fixed-budget adaptation from heterogeneous data sources requires deciding not only how much data to use, but how much exposure each source receives. Size-proportional rules can crowd out small sources, whereas difficulty-only rules can chase noisy estimates or allocate residual budget to nearly saturated pools. We intr...
Reinforcement learning with verifiable rewards (RLVR) has become a prominent approach for improving language-model performance on reasoning tasks using automatically checked answers. Yet convention-matched evaluation cannot reveal whether reinforcing one reporting convention reduces adherence to a different request tha...
Together, these results show that benefiting from more validation data, outperforming NLL selection, and outperforming the final checkpoint are distinct empirical claims that require separate evidence.
These results show that reliable scene-text recognition requires VLMs to balance visual character evidence with contextual information, preserving clear text while using context mainly when the visual evidence is uncertain.
It is found that competence dominates human-directed evaluations, while many \textit{other} attributions describe humans as epistemic, cultural, or embodied subjects, and suggest that bias in agent societies should be studied not only as isolated model output, but also as a discourse process.
VeriOCRBench, a 1,800-sample human-verified benchmark built from source images drawn from 8 OCR-related datasets and spanning 8 real-world image domains, with controlled, image-grounded diagnostic tasks, is introduced, exposing a critical reliability gap in current OCR reasoning systems.
RPCBench, a benchmark for evaluating Recommender-Premise Critique: the ability to detect, diagnose, and properly handle faulty premises in natural-language recommendation requests, is introduced and a fine-grained evaluation framework that measures proactive detection, error localization, post-detection handling strate...
This work reveals a paradox where a simplified, router-free multi-head model with high inter-head redundancy outperforms complex, diversity-driven baselines and proposes Align-LoRA, a unified and efficient framework that shifts the focus from architectural isolation to representation alignment.
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.
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