arXiv:2609.22223v1 Announce Type: cross Abstract: Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing c...
arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.
arXiv:2609.37349v1 Announce Type: cross Abstract: Multi-step visual retrieval-augmented generation (RAG) answers complex questions by repeatedly retrieving visual evidence, updating an intermediate s...
arXiv:2512. 11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- leading to unsupported answers, hallucinations, and reliance on spurious context.
arXiv:2610.16441v1 Announce Type: cross Abstract: Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Exp...
arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.
arXiv:2607. 19345v1 Announce Type: cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier.
DynaKRAG is a unified framework that learns a state‑conditioned policy to control evidence acquisition in multi‑hop retrieval‑augmented generation. It uses a deterministic validity layer to build an action set, a learned continuation gate to decide between generating an answer or gathering more evidence, and an advantage scorer to rank evidence operations by predicted gain. Across HotpotQA, 2Wiki, and MuSiQue with various backbone models, DynaKRAG achieves top EM and F1 scores, improves token and retrieval efficiency, and enables terminal evidence compression that reduces context size while boosting answer quality.
arXiv:2604. 22565v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts.
arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back t...
arXiv:2608.31005v1 Announce Type: new Abstract: Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurr...
ScienceJudge introduces Pattern Perturbation Augmentation (PPA) to generate hard negative examples for sentence‑level scientific claim verification by modeling reporting errors as faithfulness‑breaking transformations. PPA applies these patterns to supported claims, creating controlled hard negatives and expanding coverage through pattern‑level, operation‑level, and cross‑domain augmentation. The resulting ScienceJudge system, which ensembles PPA‑trained vision‑language predictors with instruction‑guided LLM predictors, achieves state‑of‑the‑art performance, improving the average score from 0.8124 to 0.8692 and ranking first on the NLPCC 2026 Task 10 Track 1 leaderboard.