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Remote multimodal models offer strong numerical reasoning capabilities over charts and speech, but sending private inputs risks exposing sensitive content. Text-only sanitization cannot directly satisfy fixed media interfaces, while identity anonymization leaves the underlying task content exposed. We introduce ReCast,...
D3-Omni is introduced, a balanced and decoupled benchmark for diagnosing fine-grained multimodal understanding, covering 53 orthogonal binary dimensions and 10,671 samples across the three tasks, suggesting that aggregate accuracy may hide systematic blind spots that a balanced and decoupled lens can help expose and, i...
RubricRM is proposed, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images.
This work recasts vulnerability discovery as an input-prediction task with a closed, deterministic ground truth, and decomposes discovery into three task modes over 22 real-world C/C++ programs spanning 15 domains, finding constraint inference, not navigation, is the dominant bottleneck.
FinIndices is a large-scale benchmark evaluating data-processing fidelity over uncropped financial statements (up to 32K tokens) and yields substantial zero-hint gains, validating that structured logic can be partially restored via data-centric alignment.
Socratic-Geo is proposed, a fully autonomous framework that dynamically couples data synthesis with model learning through multi-agent interaction, and establishes new state-of-the-art for open-source models, sur-passing strong baselines.
Results support treating PHD as a distinct target for evaluating how LLMs formulate investigative directions when final conclusions are withheld, with gains for several lower-performing models on HypoArena but regressions for other systems, including a top-performing model.
Solution Hacking is identified, a failure mode in which an LLM reaches the correct answer through invalid shortcuts, such as numerical search, enumeration, guessing, or answer-first verification, without providing a valid task-targeted derivation.
A diagnostic benchmark for open-domain, open-form, long-horizon counterfactual causal reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios, and finds that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62% final score.
Living-Harness is proposed, a self-evolving agent harness that converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates, and supports retrieval-only reuse of the evolved harness state across model backbones.
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