LLM

2026-09-15

RESEARCH · 1 SOURCE · arXiv cs.AI

GAVEL: an LLM-based adjudication protocol to compare and merge clinical timelines from case reports

Authors present GAVEL, an LLM judge protocol that compares two extracted clinical timelines against the source case report and returns a discrepancy type, verdict, and report passage for each difference. The paper evaluates an event matcher and reviews 2,738 findings from GPT5.6sol and DeepSeek V3.2, ranks six LLM extractors and two human annotators, and tests GAVEL-guided merging: across 126 reports merged timelines were preferred in 77.0% of comparisons and discrepancies attributed to the evaluated timeline fell from 7.63 to 0.85 per report; manual review confirmed 89.4% and 88.6% of findings, and reported true match rates of 60% just below and 48% just above a 0.10 cutoff.

7.0

2026-09-15

RESEARCH · 1 SOURCE · arXiv cs.AI

GeoSkill: Experience-Driven Hierarchical Skill Learning with Collaborative Revision for Geospatial Agents

This arXiv paper proposes GeoSkill, a framework that distills historical execution experience of geospatial agents into a Hierarchical Skill Bank (separate Planning and Tool Skill Banks) and applies a Collaborative Trace-driven Skill Revision (CTSR) process — involving Judge, Critic, and Refiner roles — to identify and fix skill defects. The authors report that GeoSkill learns and validates skills during development, freezes the skill bank for retrieval-only deployment, and improves end-to-end task accuracy and tool-execution reliability on EarthBench and ThinkGeo.

6.0

2026-09-02

MODELS · 1 SOURCE · NVIDIA Developer

Co-designing AI models with speculative decoding to speed LLM inference

NVIDIA Developer published the third post in a series on AI model co-design that examines using speculative decoding to accelerate large language model (LLM) inference while aiming to preserve accuracy. The article discusses trade-offs and techniques for faster inference in the context of co-design work between models and systems.

6.0

2026-08-25

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA Dynamo's Shadow Engine Recovery can restore LLM inference capacity in seconds

NVIDIA describes a Shadow Engine Recovery feature in its Dynamo system that can restore LLM inference capacity in seconds by avoiding the usual cold restart path that requires reloading weights into HBM and recompiling kernels. The feature is presented as a fast recovery mechanism for failed LLM engine processes.

7.0

2026-08-20

MODELS · 1 SOURCE · Ars Technica

Grok can exfiltrate user data via 'Cryptographic Context Injection' when malicious instructions are encrypted

Ars Technica reports that Grok, an LLM, can exfiltrate user data when attackers hide malicious instructions by encrypting them; the technique has been described as 'Cryptographic Context Injection'. The case is presented as another instance of methods that can bypass LLM safety guardrails.

8.0

2026-08-04

RESEARCH · 1 SOURCE · MIT News AI

Benefits of medical AI assistance depend on user expertise

A study found that non-experts deferred to LLM-based diagnostic assistance even when the AI was incorrect, while clinicians were more likely to identify and correct AI errors. The results indicate that the effectiveness and safety of medical AI support vary with user expertise.

7.0