#agents
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MedEvoEval: A Longitudinal Framework for Evaluating Doctor Agents
MedEvoEval is a new evaluation framework that moves beyond static medical QA by testing how doctor agents learn, retain, and adapt clinical decision-making skills across sequences of simulated outpatient episodes.
HyphaeDB: Moving From Passive Storage to Agent-Native Memory
HyphaeDB reinterprets HNSW graph topology as a communication fabric for multi-agent systems, enabling knowledge propagation and emergent consensus rather than just passive retrieval.
Agentic Abstention: Improving When LLM Agents Should Stop
LLM agents often fail to stop when a task is impossible, leading to unnecessary tool use. The CONVOLVE method improves timely abstention by distilling interaction trajectories into reusable stopping rules.
Agent Safety Is Action Alignment, Not Content Refusal
Treating agent safety like chatbot content moderation is a category error. True agent security requires enforcing least privilege at the action boundary, not training models to refuse requests.
Making LLM Self-Evolution Safe with Held-Out Selection
RSEA improves LLM agent performance by recursively evolving natural-language artifacts while using a strict held-out validation gate to prevent performance regression.
ATHENA-R1: An AI Agent for Iterative Biomedical Treatment Reasoning
ATHENA-R1 is an AI agent that performs iterative treatment reasoning by dynamically querying a universe of 212 biomedical tools, outperforming GPT-5 by significant margins in clinical benchmarks.
GPTNT: A Real-Time Collaborative Benchmark for AI Agents
GPTNT uses the game 'Keep Talking and Nobody Explodes' to test AI agent collaboration under time pressure, revealing critical failures in state tracking and real-time communication.
The Hidden Costs of AI Agentic Loop Engineering
AI agentic loops are powerful for isolated, deterministic tasks but dangerous for complex, high-context environments where they can propagate errors and inflate costs silently.
Building Great Agent Skills: The Missing Manual
To escape 'skill hell,' developers must treat agent skills as structured, maintainable code by optimizing triggers, minimizing context bloat, using 'leading words' for steering, and aggressively pruning irrelevant instructions.
Agent-Native Immune System (ANIS): Architecture for Runtime Defense
The Agent-Native Immune System (ANIS) shifts AI security from static training-time alignment to dynamic, runtime defense, using a six-layer 'Immune Tower' to protect autonomous agents against memory poisoning and tool-chain manipulation.
ATOD: Hybrid Distillation for Autonomous Agent Training
ATOD combines on-policy distillation with reinforcement learning using an annealed schedule and turn-level reweighting to train small agent models that outperform their larger teacher models.
ToE: Hierarchical Claim Verification Against Adversarial Misinformation
Tree of Evidence (ToE) is a fact-checking framework that uses a reinforcement learning-driven agent to decompose claims into hierarchical argument trees, significantly improving verification accuracy against adversarially poisoned inputs.
Improving Long-Horizon LLM Planning via Symbolic Feedback
This framework enhances LLM planning reliability by using a symbolic verifier to identify errors and provide corrective, interpretable instructions for iterative self-refinement.
AI-ModelNet: A Networked Architecture for Collaborative AI
AI-ModelNet proposes a hierarchical, Internet-inspired architecture to enable interconnection and collaborative reasoning among heterogeneous, domain-specific models, addressing the fragmentation of the current AI landscape.
Personality Prompting in Multi-Agent Teams: Task-Dependent Impact
Personality manipulation in LLM agents significantly alters communication style but only degrades task performance in open-ended or collaborative domains, while remaining largely neutral in structured coding tasks.
The Shift in Software Engineering: AI Agents and Production Risk
AI agents have fundamentally transformed software development in six months, enabling massive increases in code output. However, this shift risks quality and security when organizations prioritize AI adoption over core engineering rigor, as evidenced by recent high-profile outages.
Building and Auditing Local Coding Agents
A practical guide to setting up a local coding agent stack using Ollama and open-weight models, emphasizing performance benchmarking, secure auditing of agent harnesses, and the trade-offs of running local vs. proprietary infrastructure.
GLM-5.2: A New Benchmark for Open-Weight Agentic Coding
GLM-5.2 marks a pivotal shift in the open-weight landscape, offering the first credible, high-performance alternative to frontier closed models like Claude Opus for complex agentic coding tasks.
Claude Tag: Moving AI from Chat to Team-Based Delegation
Claude Tag shifts LLM interaction from synchronous chat to asynchronous, team-wide delegation within Slack, positioning Claude as a persistent, proactive coworker rather than a standalone tool.
SpaceX's Neocloud and the Rise of Owned Intelligence
SpaceX is emerging as a massive compute provider with $28B/year in annualized GPU rental deals, while developers increasingly prioritize 'owned intelligence' via open-weight models like GLM-5.2 to gain control over their AI stacks.
The Rise of Meta-Harnesses and Vertical AI Integration
The AI industry is shifting toward 'meta-harnesses'—standardized agent orchestration layers—while frontier labs move toward vertical integration of custom silicon and agent-native UX.
Internal AI Adoption & The Rise of Agentic Workflows
OpenAI reports massive internal token growth across all departments, signaling that agentic workflows—supported by review loops and persistent infrastructure—are moving from experimental to core production patterns.
Claude Code Changelog: Production Reliability & Agentic Control
Recent updates to Claude Code focus on hardening production workflows, improving agentic reliability through stricter permissioning and background task management, and enhancing the developer experience in terminal-based environments.
Claude Code Changelog: Production Reliability and Agentic Control
Recent updates to Claude Code focus on hardening agentic workflows through improved background task management, granular permission controls, enhanced MCP reliability, and significant performance optimizations for terminal-based AI development.
Claude Code Changelog: Production Reliability & Agentic Control
Recent updates to Claude Code focus on hardening agentic workflows, improving background task management, and refining safety controls for autonomous shell and MCP operations.
Claude Code Changelog: System Reliability and Agentic UX
Recent updates to Claude Code focus on hardening background agent reliability, improving TUI responsiveness, and refining safety controls for autonomous operations.
Claude Code Changelog: Production Reliability and Agentic Control
Recent updates to Claude Code focus on hardening background agent reliability, refining safety controls for auto-mode, and optimizing terminal performance for professional engineering workflows.
Claude Tag: Collaborative Agentic Workflows in Slack
Claude Tag integrates Claude into Slack as a persistent, multiplayer agent capable of autonomous task execution, cross-channel context awareness, and proactive collaboration.
Agentic Robotics, Large-Scale Infra, and Future Uncertainty
Recent developments in agentic robot self-improvement, large-scale GPU cluster telemetry, and legal data infrastructure highlight the rapid maturation of AI systems, even as experts debate the long-term implications for human autonomy.
Architecting an Agent-Native Immune System (ANIS) for AI Security
The Agent-Native Immune System (ANIS) moves security from external training-time alignment to an endogenous, runtime defense architecture that protects autonomous agents from hijacking and manipulation.
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