Experiments show that the rubric-based rewards reliably distinguish legal responses of different quality and that DPO training on the preference data improves performance across all three dimensions, and Dimension-wise analyses further support the effectiveness of the proposed taxonomy and reward construction.
Yi-Da Cai, Xin Dai, Bing-Xiang He et al.· 0 citations
Test-time reasoning systems often respond to failure by restarting or revising the latest step, even when an earlier decision caused the error. We introduce CORE, a search controller that requests a certified conflict core from a verifier, backjumps to the latest decision in that core, and caches the conflict to avoid...
Si-Yu Song, Rui-Peng Xu, Jia Lin et al.· 0 citations
SciCore, a dual-branch reviewer that averages a full-manuscript judgment with a judgment based on an extracted, structured science core, achieves a leading joint stability-discrimination profile among the benchmarked reviewers while maintaining competitive human alignment.
Chen-Guang Wang, Ming Li, Cheng-Rui Fan et al.· 0 citations
The authors' manual, post-hoc analysis is descriptive: in one inspected case, optical character recognition (OCR) and block grouping merged the relevant passage into another block, and the system answered from unrelated text.
Divya Godara, Sachin Gupta· 0 citations
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Settle extends the accuracy-token-count Pareto frontier of the evaluated stopping methods and predicts whether a correct answer will remain correct in completed traces.
It is proved that the clipped objective can fail to correct the student toward the teacher and can instead push clipped and unclipped token probabilities away from its teacher.
At 460 h of clean LibriSpeech, the seed moves fairness metrics more than compression does on most demographic axes, and a balanced 3x3 decomposition attributes 85.3% of the variation in Fair-Speech ethnicity normalized gap to the seed against 8.3% to compression.
Srishti Ginjala, E. Fosler-Lussier, Srinivasan Parthasarathy· 0 citations
GraphForge, an evidence-graph based framework that grounds both the task and its verification in real files, and rejects fine-tuning on the SFT model's own rollouts yields further improvements on all three benchmarks, suggesting that the rubrics provide a useful selection signal.
Qi-Sheng Su, Han-Chen Wang, Guang Zhu et al.· 0 citations
Finding a relevant paper is not the same as producing a verifiable answer from it. LitTraceQA requires canonical paper identifiers, exact evidence at the page or object level, and typed answers that match the evaluator. We call the separation between source access and scorer-visible correctness the grounding contract g...
Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. Accurate attribution on unmodified text, however, does not show whether the prediction still identifies the original source after deliberate rewriting. We formulate this...
Hao-Han Yuan, Si-Min Chen, Xi Niu et al.· 0 citations
While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems remain bottlenecked on pre-defined and hand-crafted model pools. In this work, we investigate the problem of model selection in multi-LLM systems. We propose and systemat...
Zong-Wan Cao, Zi-Yuan Yang, Shang-Bin Feng et al.· 0 citations
Overlap-Unique-Conflict (OUC) extraction, a cross-narrative task that extracts all overlapping, conflicting, and unique clauses from two narratives, is introduced, finding that unique information is far easier to extract than overlap and conflict.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.