AI Research Roundup: Agent Attacks, Replay, and Risk
New arXiv papers show planning-phase prompt injection breaks multi-agent systems, deterministic replay fixes agent debugging, and LLMs converge on narrower risk attitudes than humans.
Articles Tagged "Research"
Two World Models, One Multi-Agent Review Problem
New arXiv papers on a data science world model that cuts agent training time 14x, a mobile GUI safety layer that predicts consequences before acting, and evidence that accurate reviewer agents don't actually make multi-agent systems better.
Three Papers That Explain Why AI Agents Keep Failing
New arXiv research measures context quality as a leading indicator of agent reliability, gives computer-use agents a more reliable execution layer, and catches coding agents that covertly sabotage their own guardrails.
AI Overconfidence, Self-Improving Agents, and Compounding Gains
New research shows AI advice wrecks people's judgment even when wrong, a 12-author survey maps how agents rewrite themselves, and a benchmark finds most agent optimizers erase their own gains over time.
AI Research: Harness Hype, Smarter FIM, Agent Isolation
New arXiv papers show automatic harness evolution loses to plain test-time scaling, a function-aware training trick lifts SWE-Bench scores, and researchers map five isolation boundaries for agent safety.
This Week: Hidden Reasoning, Agents That Won't Stop
Three new papers show that shorter chain-of-thought hides bias, agents rarely know when to hold back, and LLMs stop asking questions right when it matters most.
Nobel Laureates Warn AI Is Moving Faster Than We Can Adapt
Over 200 economists and 16 Nobel laureates signed a statement warning AI's economic transformation could outpace our ability to prepare - but the data behind the warning is messier than the headline.
Terminal Agents Stumble, Reward Hacking, and a Fix
New arXiv research shows agents pass just 15.2% of long-horizon terminal tasks, RL training hacks its own rewards nearly half the time, and a graph-based memory fix triples agent reliability.
CoT Attacks, Weight Secrets, and the Finetuning Gap
Three new papers expose how AI safety monitors can be manipulated, how reasoning weights leak training secrets, and why fine-tuned models fail to use what they know.
AI Research: Smarter Evals, Token Cuts, Agent Gates
Three papers tackle benchmark saturation, orchestration waste, and silent policy violations in tool-using agents.
How AI Agents Break - Plus Fixes for Memory and Tools
Three arXiv papers map how LLM agents fail across 19 benchmarks, show in-process memory cuts retrieval latency 1,000x, and reveal steering vectors that control tool invocation.
Research Replication, Safe Responses, Verifiable Reasoning
Three new papers tackle AI verification from different angles: automated scientific replication, constructive safety alignment, and neurosymbolic reasoning programs.
