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JustFit Compresses and Dynamically Schedules KV State, Enabling a 24 GiB MacBook to Complete a 210K-Token Workload

By combining 4-bit KV, component residency management, and cross-request state transitions in MLX, JustFit enables a 27B model to perform long-context inference within 24 GiB of unified memory. The reported 6.93× capacity increase comes from a single-machine, model-specific test, and some historical environment details and checkpoint identities cannot yet be fully reproduced.

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IBM Identifies Unstable Agent Steps From a Single Trajectory as ALTK-Evolve Turns Diagnostics Into Reusable Guidelines

IBM Research has added a Consistency Analyzer to ALTK-Evolve that uses controlled resampling to pinpoint decisions prone to changing across repeated runs of the same agent. In AppWorld experiments, it raised a GPT-4.1 agent’s five-run all-success rate from 53% to 69%, though the evidence remains limited to a single agent architecture and a simulated environment.

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RSIAgent Freezes Model Weights and Builds Reusable Environment Memory Through Autonomous Exploration

RSIAgent uses curriculum, actor, and verifier agents to first explore tools broadly and then deeply test failure boundaries, saving the results as action–condition–outcome memories. Its partial score on OSWorld 2.0 rose from 71.97 to 78.98, but the comparison mixes different budgets and selectively chosen retries, so it cannot be directly interpreted as surpassing GPT-6 in model capability.

AI 安全

HazardAuditor Unifies Trajectories from Four Computer-Use Agents and Trains a Safety Gatekeeper with Sequence-Level Rewards

HazardAuditor converts tool executions from Claude Code, Codex, Hermes, and OpenClaw into a shared event format, then determines whether the overall behavior is safe. Its new GuardPO method makes the final safety verdict drive model updates, but the research data has not been released, and gains of up to 16.5 percentage points still await independent replication.

AI 安全

SWEADV Uses Malicious Issues to Mislead Repair Agents: Over Half Fix the Functionality While Introducing Vulnerabilities

A new benchmark rewrites 150 SWE-bench Verified tasks into 750 issues containing security traps, testing whether automated repair agents will deliver code that is functionally correct but insecure. Across three model backends, an average of 51.7% of cases achieved both the repair and attack objectives, while common model reviews and static analysis still missed many malicious patches.