Agentic AI is accelerating, but reliability and security remain central: LLM judges miss silent failures, MCP connectors expand the attack surface, open agentic models emerge, and high-stakes decision limits appear in oncology.
A training framework teaches grounded QA models to answer only when evidence becomes sufficient, locating the exact point where abstention gives way to a response. On HotpotQA, 2WikiMultiHopQA and MuSiQue, with Qwen2.5-3B-Instruct and LoRA, the metho...
PRO-Step introduces step-level supervision for Retrieval-Augmented Generation. Instead of evaluating only the final answer, a generative process reward model checks logical validity and evidential grounding at every step of multi-hop reasoning. The m...
A study across 400 trajectories shows outcome-only LLM judges catch 84% of loud faults but only 45% of silent ones while flagging 33% of correct trajectories. A step-rubric judge reaches 77% silent recall with zero false alarms at 3x cost. No judge r...
OpenAI previewed precautions for Astra, its new cyber-critical LLM described as highly capable of compromising computer systems. The announcement shifts the security conversation: for on-premise and self-hosted deployments, a model's offensive capabi...
MCP became the default connector for AI agents in about a year, but it brings an attack surface that traditional firewalls don't cover. Between tool poisoning, rug pulls, and over-permission, the real issue is trust in metadata. Security vendors are ...
ERR+ introduces a two-phase RLVR framework that rewards token-level entropy drops and relative length efficiency. Correct traces show more frequent and larger entropy drops during thinking. Tests on five datasets report improved accuracy and concisen...
Nine frontier LLMs evaluated on 2,005 oncology decision points show a collective boundary: in 42.1% of items none answers correctly, especially when choosing between guideline pathways. Two decisiveness-tuned models make unsafe commitments 3–5 times ...
DS-Lighting is an open-source toolkit that makes the agent harness explicit for data-science automation. It decomposes the harness into four reusable layers—data, workflow, execution, and evaluation—and represents agents as executable operator progra...
Z.ai uses the same base model as GLM-5.2 for GLM-5.3, but all gains come from post-training. Coding improves by 50% on the internal Code Bench and reaches state of the art on Terminal Bench 3.0 and Agents' Last Exam. The real shift is the emergent cy...
A new white-box fuzzing framework replaces response-level feedback with a continuous score derived from safety neurons during prefill. Across 21 models it discovers jailbreaks in 76-100% of cases and transfers optimized templates to open-weight and p...
The Apodex team's AMA on r/LocalLLaMA introduces Apodex 1.1, an open model family for agentic work, plus an open-source harness and two papers. Quantized variants in FP8, GPTQ-Int4, and NVFP4 point to local, self-hosted inference, but no benchmark fi...
The Apodex team has presented Apodex 1.1, an open model family designed for complex work involving reasoning, search, code execution, and multi-agent coordination. Alongside the models, the team released FrontierAgent and two papers. The NVFP4, GPTQ-...
