Instruction duplication is a simple, inference‑time control that repeats the procedural instruction in a language‑model output without retraining or decoding changes. In experiments across seven instruction‑tuned models on 300 medical multiple‑choice questions, duplicating the instruction increased the proportion of deterministic All‑8 diagnostic‑responses from 90.22 % to 93.17 % and reduced failures by 30.2 %. The technique also improved pre‑provisional TF‑IDF recall and, in downstream Answer Engineering scenarios, significantly raised success rates for specific endpoints.
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
The paper introduces a framework for evaluating how large language model agents revise their success criteria after failures, defining five non‑compensatory conditions that must be met for a criterion revision to be considered valid. Using the CMB‑0.1 protocol, the authors test twelve cross‑domain scenarios across four system configurations, finding that no model trial satisfies all five conditions and highlighting specific failure modes such as zero‑state reconstruction and inadequate intervention sensitivity. They propose a more stringent trace‑anchored CMB‑0.4 protocol to better isolate and measure criterion revision in future studies.
By Guodong Xu
The study investigates how language models equipped with tools can still produce unsupported final claims, even when a single tool call could resolve the uncertainty. It defines two metrics—occurrence (how often unsupported claims arise) and conditional repair (how often they are fixed when evidence is provided). Experiments on Qwen3-32B and Gemma 4 show that providing the missing evidence consistently repairs all unsupported claims in the Qwen3-32B setup, while the Gemma 4 model never produced unsupported claims under the tested conditions.
By Justin Bronder
The paper investigates whether targeted edits to a few internal components of Gemma 4 instruction‑tuned models can reduce persistent repetition loops that occur during long factual enumeration prompts. By combining per‑layer ablation with per‑neuron attribution, the authors identify specific neurons whose weight edits dramatically lower loop frequency—one sign‑inverted neuron suffices for Gemma 4 E2B. Across all four Gemma variants, loop occurrences drop from 46/384 to 12/384 on held‑out prompts, while general‑purpose benchmarks show no significant regressions. The study also demonstrates that similar sparse edits can mitigate repetition in other families such as Qwen3.5 and LFM2.5, though the effect varies.
By Aristotelis Lazaridis, Aman Sharma, Dylan Bates, Brian King, Vincent Lu, Jack FitzGerald
arXiv:2608. 19893v1 Announce Type: cross Abstract: Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see?
By Roberto I. Ono Filho
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 introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides a structured framework that uses paired evidence, protected-slice monitoring, and trace-level localizability to decide whether to promote, flag, or reject memory interventions. The authors also present DCR, a metric for measuring failure localizability, and D$^2$ACCI‑Eval, a reusable artifact for gate replay, demonstrating significant performance gains on three public benchmarks and highlighting the importance of traceable, statistically grounded diagnostics.
By Xule Liu, Yijun Liu, Chao Li, Shao Kun
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
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
By Alex Kwon
arXiv:2608.23224v1 Announce Type: cross
Abstract: Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a con...
By Zhiruo Zhou, Zelin Li, Xiwen Chen, Jiazhuo Li, Chenwei Wang, Huiming Chen, Xiaojun Zhu
The paper reports on a series of experiments examining how different forms of directives—such as record pointers, criteria, or combinations—affect an agent’s choice of archived source records when it inherits six one-line memories. Across twelve registered studies involving 14,760 attempts on a single instrument lineage, the authors measured the impact of various directive formats on six direct-provider models, nine OpenRouter-served models, and several Claude and Opus 5 models, noting differences in performance metrics and replication outcomes. The results are purely descriptive, detailing the effects of exact edits on fixed panels with registered intervals and no claim of underlying mechanisms.
By Kazuki Nakayashiki