The paper investigates how periodic subject changes—termed interruptions—affect the perceived novelty and coherence of text generated by base language models. By inserting a new subject every few hundred tokens into a stream that otherwise repeats, the authors find that judged surprise increases by 1.2 to 1.4 points and connection by 0.8 points compared to habituation alone. The study also reports that such interruptions do not produce integrated documents, and that the effect is robust across different models and evaluation protocols.
arXiv:2607. 20972v1 Announce Type: new Abstract: Coding agents ship with one kind of memory: documents.
By Swapnanil Saha
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
By Alex Kwon
A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.
The paper demonstrates that a prompt’s influence is not inherent to the prompt itself but depends on the model, as prompts optimized for one model degrade on another and rankings shift under neutral reformatting. By examining a task‑free structural readout—specifically the fixed‑point behavior of a short‑window argmax map—the authors show that nine tokens of conditioning can move the fixed‑point fraction across most of its range, altering structural classes and model rankings, while instruction tuning has no effect. Attempts to explain this phenomenon through prefix length, content type, bidirectionality, or attention‑sink dominance all fail, indicating that the prompt‑model pair is the fundamental unit of explanation.
whyItMatters":"The study reveals that prompt effectiveness is model‑specific and that simple structural readouts can capture this interaction, challenging assumptions about prompt generality and guiding future prompt‑engineering efforts."
By Nicol\'as Vera Z\'u\~niga
arXiv:2606. 31495v1 Announce Type: new Abstract: We study a single idea across two settings: that a prediction-error signal, computed by a small predictor over the latent space of a frozen encoder, can serve both as a gate on plasticity and as a substrate for metacognition.
By Louis Mouchon
The study investigates why small language model agents tend to repeat a tool call that just failed. By recording the failed call and its error message in the transcript, the authors measure a negative corrective gain—agents are more likely to repeat the failed action, with a drop of about 1.03 nats per token. The problem is traced to the harness design rather than the model’s understanding of errors, and the authors show that replacing the verbatim call with a runtime-generated description of the failure can reduce this backfiring effect by 76%.
By Esmail Gumaan
arXiv:2606. 13705v1 Announce Type: cross Abstract: Yes.
By Aristotelis Lazaridis, Aman Sharma, Dylan Bates, Brian King, Vincent Lu, Jack FitzGerald
arXiv:2603. 00270v3 Announce Type: replace-cross Abstract: Large language models can process millions of tokens, yet how they handle conflicting information within context remains poorly understood.
By Sourav Chattaraj, Kanak Raj
arXiv:2607. 10441v1 Announce Type: cross Abstract: Context engineering decides what information a model carries forward, and current designs meter it in tokens: compressing the past into a bounded recurrent state, keeping a key-value entry for every token, or imposing a fixed budget through a window or eviction rule.
By Siddharth Pal, Viktoria Rojkova
arXiv:2608. 03297v1 Announce Type: new Abstract: A standard claim in the literature on retrieval-augmented and memory-augmented language models is that shorter context is better when the relevant information is preserved.
By Mohsen Arjmandi
arXiv:2607. 19257v1 Announce Type: cross Abstract: Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (markdown, plain text, prose, or tabular), how many simultaneous instructions a system prompt can carry before compliance degrades, and how much context a model can hold before recall and honesty degrade.
By Netanel Eliav