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Agents2026-07-26 · source-backed
A July 21 paper pairs two near-identical agents: an Explore Agent that inspects untrusted input but holds no tools, and a Safe Agent that takes privileged actions using its own context plus length-constrained hints from the explorer (arXiv 2607.19595). Borrowing from residual coding, longer hints raise both task utility and injection risk, so you tune the tradeoff explicitly rather than discovering it in production. It beat both undefended agents and prior privilege-separation baselines on the utility/security frontier across SWE-bench Lite and AgentDojo.
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arXiv 2608.11878 replaces the handful of manually implemented injection-testing environments with an Environment Simulator, Attacker Agent, and User Simulator that generate executable stateful environments and discover viable injection points automatically. Injection timing an...
Paritok-4B (arXiv 2608.24188) is a LoRA on Qwen3-4B distilled from a gpt-4.1-mini teacher over 67,074 real OpenHands trajectories. It's extractive rather than paraphrasing, with 96.0% of emitted identifiers, paths and numbers already present in its input, and intent-conditione...
Coding agents ace correctness benchmarks and flail at repository-level performance work, because bottlenecks hide behind abstraction layers and the agent stops at the first passing patch. PerfAgent wraps an off-the-shelf agent with a profiler-guided, verifier-in-the-loop workf...
While the capability stories pile up, here's the counterweight. As SWE-bench Verified scores cluster near saturation on July leaderboards, an enhanced analysis (SWE-Bench+, on the AIware 2026 benchmark track) found 60.83% of commonly resolved issues contain solution leakage ri...
arXiv 2608.05144 runs Manager, Planner, Engineer and Reviewer roles over persistent project state with *fixed* model weights, self-evolving through runtime state and control policies rather than training. 76.8% on AARRI-Bench, and mature waves use 21% fewer solve-input tokens...
Microsoft's July 23 release targets a genuine gap: harness-based agents like Claude Code and Codex drive multi-turn reasoning, tool use, and external system access but were hard to train end-to-end with standard open RL infrastructure. The trick is decoupling training from inf...
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