arXiv:2607. 19088v1 Announce Type: cross Abstract: Chain-of-thought (CoT) supervision exposes intermediate rationales, but flat rationale targets usually optimize a single reasoning sequence and provide limited supervision on how local conclusions should support later decisions.
By Yu Wang, Ming Fan, Xicheng Zhang, Zhiyong Li, Zhihu Wang, Caiyue Xu, Dahai Hu, Ting Liu
Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult because partial evidence can make an unsupported a...
The paper introduces Evidence Sufficiency Boundary Training, a framework that teaches models to abstain from answering until the supplied evidence is fully sufficient, and to remain stable when additional redundant evidence is added. By constructing ordered evidence chains from datasets such as HotpotQA, 2WikiMultiHopQA, and MuSiQue, the method applies level supervision, a boundary flip margin, post‑boundary stability, and answer recall protection. Experiments with Qwen2.5‑3B‑Instruct and LoRA adaptation show improved boundary localization (flip accuracy 0.807 vs 0.781) and a lower unsupported‑answer rate (0.095 vs 0.101) while maintaining competitive raw QA F1.
By Haruto Sato, Yuki Tanaka, Ren Nakamura, Aoi Kobayashi, Mei Ito
The paper introduces the Unified Memory Agent (UMA), a system that builds a query‑agnostic external memory from a data stream and reuses it across multiple question‑answering sessions. UMA employs a single policy to manage a structured Memory Bank via CRUD operations and uses Task‑Stratified GRPO to supervise memory maintenance based on QA trajectory rewards. The authors also present Ledger‑QA, a benchmark for long‑horizon state tracking, and demonstrate that UMA outperforms other methods on test‑time learning and accurate‑retrieval tasks, with UMA‑Specialist further improving performance after task adaptation.
By Kehao Zhang, Shangtong Gui, Sheng Yang, Wei Chen, Yang Feng
arXiv:2501. 06286v2 Announce Type: replace-cross Abstract: Multi-hop question answering requires a system to identify and integrate evidence distributed across documents, yet large language models remain vulnerable to irrelevant context.
By Iman Barati, Arash Ghafouri, Behrouz Minaei-Bidgoli
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, most existing CoT methods use reasoning chains mainly as inference-time prompts, while the generated reasoning traces are rarely reused as semi-supervised learning signals.