AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Agent Lightning v1.0
Modern agents run inside a harness that owns tools, context, and control flow. When you want to train one, that ownership becomes the problem: the harness runs the environment loop and the trainer only ever sees LLM request and response pairs. This work from Microsoft treats that boundary as the integration point instead of an obstacle.
The Skill Trigger Bottleneck
There are 56,804 public agent skills today, all competing for fewer than 100 reliable trigger slots in the system prompt. Your own playbooks compete for that same space, which means the long tail never gets used no matter how good it is. The paper argues the scarcity is an artifact of how skills get installed, not a real limit.
Harness-Level Forgetting
Continual learning has always tracked what changes in the weights. Modern agents accumulate their experience somewhere else entirely, across prompts, memories, tools, skills, and routing rules, and nobody has been measuring what happens when that layer drifts.
The Control-Plane Tax
Serving systems for agentic applications are still built on assumptions inherited from single-turn LLM inference. This work instruments ten real agentic applications end to end and finds the model is often not what your latency bill is paying for.
Demystifying Agent Skills
Skills are usually assumed to inject knowledge the model lacks. This paper runs the controlled comparison and finds that assumption is almost entirely wrong, which changes what a good skill should contain.
Strategy Lock-In
Agents post-training other agents is one of the more load-bearing assumptions in current recursive self-improvement arguments. This paper analyzes a large corpus of publicly released post-training trajectories to see whether the loop actually closes, and finds a specific structural failure.
SocialRL
The dispositions that make an assistant pleasant make it a poor delegate. A friendly frontier model volunteers its principal's private information and concedes at the first sign of resistance, which is exactly the wrong behavior when it is negotiating on your behalf.
ClawGym II
If you want to train agents inside the harness they already run in, this is the black-box version of that idea. ClawGym II runs RL through OpenClaw and Claude Code as opaque boxes, with a serving proxy at the model boundary capturing every call the harness makes, then organizing those calls into prefix trees so PPO and GRPO can optimize over the recovered multi-turn structure. Qwen3-30A3B gains 9.98 points of Pass@1 through OpenClaw and 14.81 through Claude Code, stable across 200 to 400 optimization steps. Mix-harness training pushes further: one model optimized jointly by heterogeneous harnesses, which points at policies that generalize across execution systems instead of overfitting to a single one.
Inside an Agent Team
Naming one agent the coordinator creates no communication hub and gives no reliable improvement in success, which is worth knowing before you architect another supervisor pattern. Researchers instrumented 1,902 multi-agent coding runs as temporal networks, with agents and files as nodes and messages, writes, and reads as timestamped edges carrying cost. Direct messaging grows close to quadratically with team size, much of it from an early round of introductions, then saturates in the largest teams as agents switch to broadcast. Task shape drives topology: shared-specification work produces dense connected teams while pipeline tasks produce sparse networks organized around local interfaces. Swapping repeated one-to-one messages for shared files cut output tokens about 42% at eight agents on message-heavy work. Separately, agents sought out hidden grading material unprompted, and in a sealed rerun across 244 runs with marked placeholder files they still reached for it in four fifths of runs.
The Fragility of Self-Improving Agents
Memory-based self-improving agents report gains that have never been checked against evaluation noise. This re-evaluation adds the two things prior work skipped, multiple runs to measure variance and randomly shuffled task orders, and both hurt. Agent evaluation is already noisy on multi-step tasks, and stacking a self-improvement loop on top amplifies that noise rather than averaging it out. The sharper finding is that default task orderings impose an implicit curriculum, and much of the reported gain was riding on it. Adding detailed rubrics and environment feedback to memory construction recovers part of the drop, and a significant gap remains. If you are measuring your own memory loop, shuffle the task order first.