Paper

Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration

Unreadunread

AI summary

arXiv:2605. 31365v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents.

Reading status

Priority

About this paper

Authors: Weile Chen, Bingchen Miao, Qifan Yu, Wendong Bu, Guoming Wang, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Siliang Tang
Released: 2026-06-01 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: line, rate, lora, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.31365

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.