AMEND is an inference‑time framework for text detoxification that separates detoxification into role‑conditioned proposal generation, isolated multidimensional criticism, and constraint‑first selection. It evaluates diverse candidates with role‑blind critics, enforces strict semantic and fluency feasibility, and ranks them by utility. The lightweight AMEND Lite variant limits the search to a single targeted repair round when residual toxic spans remain, achieving higher joint scores and reducing inference requests by 28.4% compared to unconstrained multi‑round loops.
arXiv:2602. 02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale.
The paper introduces Transition-Aware Reward-Guided Representation Sampling (TARGRS), a test-time detoxification framework for large language models that learns reward guidance over toxicity transitions in latent representation space. TARGRS constructs trajectories from non‑toxic to toxic states, uses toxicity scores along these paths to train a token‑level reward model, and then guides coarse‑to‑fine sampling in the opposite direction to produce detoxified outputs with minimal perturbation. Experiments on ten LLMs and three LVLMs show that TARGRS consistently outperforms existing baselines, reducing toxicity by up to 69.38% on RealToxicityPrompts and up to 64.23% on LVLMs while maintaining near‑original speed and core capabilities.
The Writerslogic team participated in the CLEF 2026 SimpleText shared task, tackling both text simplification (Task 1) and complexity spotting (Task 2). For simplification, they built a multi‑candidate pipeline with GPT‑4o‑mini, selecting the best candidate via a reference‑free heuristic, and their Claude Sonnet 4 submission achieved a SARI of 47.43 and BLEU of 14.21, ranking third overall on the Task 1 leaderboard. For complexity spotting, they fine‑tuned a DeBERTa‑v3‑large NLI model on 350 K labeled pairs, achieving a macro F1 of 0.8081 (0.8085 in an ensemble) on binary over‑generation identification and 0.804 accuracy on multi‑class error classification, placing them second among unique teams.
arXiv:2609.39027v1 Announce Type: new Abstract: AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimiza...
DeepSafe‑SciHazard introduces a real‑world‑grounded benchmark for measuring scientific safety risks, comprising 2,400 hazardous and 600 oversafety questions across 12 disciplines. It defines a decomposed harm scoring system, DeHarm‑Score, that evaluates query severity, refusal behavior, and response‑level risk, further breaking down harm into executability and net‑new risk. An expert‑validation study shows DeHarm‑Score aligns 90.17% better with expert judgments than the best baseline, and the benchmark reveals that deep research agents score 32.3% higher on mean DeHarm‑Score than standard LLMs, highlighting a critical blind spot in current safety defenses.
The paper introduces an LLM-as-a-Judge framework for evaluating the outputs of an agentic drug discovery assistant, ChatInvent, deployed at AstraZeneca. It defines four quality dimensions—Completeness, Relevancy, Structural Clarity, and Scope Adherence—alongside deterministic Tool Call Correctness checks, and validates the judge against five expert annotators. After optimizing the best-performing judge with few-shot demonstrations, alignment with human majority votes improves from 0.80 to 0.86, and the framework reveals that informal question phrasing does not degrade output quality.
arXiv:2605.26911v2 Announce Type: replace Abstract: LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and...
arXiv:2607. 18665v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance.
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely on templated queries disconnected from real-world hazards, and employ LLM-as-a-Judge paradigms without domain grounding.
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
Goldsmith is an agentic pipeline that transforms a small set of expert-annotated calibration examples into a reusable structured annotation definition treated as a trainable textual object. Candidate definitions are evaluated on the same gold examples using an executable structured loss, and a large language model editor revises high-loss failures only when loss decreases. The approach outperforms direct rewriting, OPRO, APE, and PromptBreeder in prompt-optimization tests and enhances downstream annotation across typed span, pair-level relation, and fixed-trigger event-argument tasks when combined with retrieval, score-based routing, and human review.