Agents24d ago
RunningTab: KAIST Overcomes Coding Agent Context Drift via Environment-Side Tabs for Direct Workspace Interaction
In software engineering and knowledge-work tasks, autonomous coding agents operate via Direct Workspace Interaction (DWI)—issuing shell commands to navigate, inspect, and modify files within local repositories without external vector indexing. However, during extended interaction horizons, agents suffer severe context degradation: initial task requirements are purged from context windows by voluminous file dumps, read file provenances vanish, and listed but unopened candidate files are permanently forgotten, leading to premature task terminations and unfulfilled requirements. Researchers from KAIST introduce RunningTab (arXiv:2610.10444), an innovative agent interaction framework that replaces unreliable internal agent scratchpads with an environment-maintained 'running tab'. The tab persists three critical streams: dynamic requirement trackers, verified file excerpts with provenance links, and unopened candidate file stacks. When an agent attempts to finalize a deliverable, the environment triggers a mandatory 'Finish Check' barrier that blocks completion if unsatisfied criteria persist. Evaluated across Workspace-Bench, TheAgentCompany, and OfficeQA Pro benchmarks using frontier LLMs, RunningTab decisively outperforms vanilla DWI and self-refine baselines, establishing substantial gains in rubric satisfaction (TCR@70).
Release24d ago
Codex Day 7: GPT-6.1 Sol default context rises from 272K to 500K, no long-context fees on subscription usage
OpenAI's Codex lead Tibo says GPT-6.1 Sol's default context in Codex and ChatGPT Work goes from 272K to 500K (~80% more) to cut compactions on long tasks, with no long-context fees when usage comes from a ChatGPT subscription. Separately, the official Codex changelog says GPT-5.5 retires from ChatGPT and Codex on all plans on October 14 (the API keeps it).
Research37d ago
UW & Meta FAIR Introduce Context Language Models (CLMs): Managing Context as Editable Files Boosts Agent Swarms by 65%
As autonomous software engineering agents operate across multi-hour and multi-day horizons, context management becomes a decisive bottleneck. Prevailing paradigms treat context as an external append-only log, managed through rigid heuristic pruning outside the model's awareness. Researchers from the University of Washington, Meta FAIR, and Ai2 introduce Context Language Models (CLMs), where language models natively manage their own context by treating it as an editable file subject to unrestricted in-situ read/write operations. Zero-shot CLMs decisively outperform state-of-the-art context management: scoring 5% higher with 59% fewer FLOPs on 12-hour EdgeBench, and delivering a 65% improvement at matched compute on a 24-hour multi-repository agent-swarm task. Co-designed with SGLang, a novel Suffix Cache Reuse serving mechanism reduces inference compute by 35%, charting a scalable path for autonomous long-horizon multi-agent systems.
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