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Paper reports bit-flip attack that can drive mixture-of-experts LLMs into long generation loops
A paper accepted at EMNLP 2026 reports that flipping routing-layer bits in mixture-of-experts language models can sharply inflate output length, potentially creating an availability and inference-cost risk.arxiv.orgInnovation
Claim-locked reporting aims to keep AI-generated scientific claims tied to evidence
An arXiv preprint proposes fixing a report’s evidence, numbers, directions and permitted wording before an LLM generates connective prose. The authors report higher cross-run reproducibility than a hybrid template in fMRI and randomized-trial reporting, alongside lower observed token use and median latency in one…arxiv.orgInnovation
ClueWeaver uses dual-agent reasoning to help compact LLMs analyze long narratives
A newly submitted paper presents ClueWeaver, a reward-trained framework that divides long-narrative question answering between evidence retrieval and interpretation in compact, locally deployable language models.arxiv.orgInnovation
GRIN paper proposes reinforcement learning for continual knowledge injection in language models
A new arXiv paper proposes Golden-GRPO Injection, a three-stage self-learning framework intended to help large language models absorb newly injected knowledge and apply it across paraphrases, combined documents and reasoning tasks.arxiv.orgInnovation
BanglaMamba trades some accuracy for faster, lower-memory Bangla fake-news detection
A new preprint reports that BanglaMamba, a state space model for Bangla fake-news detection, uses less GPU memory and processes data faster than tested BERT-based systems, while trailing BanglaBERT on accuracy and external-dataset performance.arxiv.orgSecurity
Preprint finds persuasion can spread through LLM agent networks
A new preprint reports that direct messages and peer relays can shift the stances of language-model agents, and argues that evaluations should track exposure histories and actions, not text alone.arxiv.orgInnovation
Preprint finds common multilingual AI metrics can skew cross-language comparisons
A new preprint reports that several normalized metrics used to compare language models across languages can introduce biases linked to tokenization, encoding and orthographic differences. It argues that sentence-level negative log-likelihood over semantically equivalent sequences produces more consistent comparisons.arxiv.orgInnovation
MTDiag tests whether medical LLMs can reason across multi-turn diagnostic conversations
A new dataset evaluates large language models as diagnostic agents in interactive clinical encounters rather than through isolated questions. MTDiag combines cases from emergency medicine, clinical records and published case reports, and proposes knowledge-grounded metrics beyond diagnostic accuracy.arxiv.orgInnovation
HealthBench-Psych introduces an open benchmark for mental-health AI performance
Researchers created HealthBench-Psych, a 610-conversation mental-health subset of OpenAI’s HealthBench, and released its evaluation pipeline, model responses, grades and analysis code.arxiv.org
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