Feedback Loops

TL;DR for operators An autonomous agent that likes recommended content can change its own future information environment. The Like Trap: Multi-Stage Poisoning against Agents in Similarity-based Recommendation Systems1 shows how an attacker can exploit that loop in a similarity-based recommender: early poisoned posts are close enough to the user profile to be surfaced and liked, then those likes increase the scores of later poisoned posts that are progressively less similar to the user. ...

TL;DR for operators Code agents do not become reliable because they are asked politely to “fix the bug.” They become more useful when they are placed inside a loop that can run their output, return structured failure evidence, and decide how many further attempts are worth buying. That is the practical point of Zhang and Kothari’s paper, Unlocking LLM Code Correction with Iterative Feedback Loops.1 The authors evaluate four LLMs across Python and Java using LeetCode problems, then move from ordinary one-shot performance to an automated correction loop: generate code, execute it, feed back compiler/runtime/testcase information, and repeat up to ten iterations. ...