Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2% to 52.0% with Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with TTS. The code is available at https://anonymous.4open.science/r/SWE-World-2D1E.

Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from “overthinking”, producing excessively long reasoning traces that increase latency and memory usage. Existing LRMs typically enforce conciseness with uniform length penalties, which over-compress crucial early deduction steps at the sequence level and indiscriminately penalize all queries at the group level. To solve these limitations, we propose PACE, a dual-level framework for prefix-protected and difficulty-aware compression under hierarchical supervision. At the sequence level, prefix-protected optimization employs decaying mixed rollouts to maintain valid reasoning paths while promoting conciseness. At the group level, difficulty-aware penalty dynamically scales length constraints based on query complexity, maintaining exploration for harder questions while curbing redundancy on easier ones. Extensive experiments on DeepSeek-R1-Distill-Qwen (1.5B/7B) demonstrate that PACE achieves a substantial reduction in token usage (up to 55.7%) while simultaneously improving accuracy (up to 4.1%) on math benchmarks, with generalization ability to code, science, and general domains.

While prior work focuses on treating answer verification as a general reasoning or semantic matching problem, significant challenges remain in computation-oriented scientific domains. In this paper, we introduce CoSineVerifier, an agentic verifier that actively invokes external executors to ground its judgments in precise computations and symbolic reasoning. To train this accurate agentic verifier, we propose a novel verifier training data augmentation method to increase the correctness and efficiency of tool-invoked verifications. Extensive experiments across STEM, QA, and long-form reasoning tasks demonstrate CoSineVerifier’s robust generalization abilities, achieving state-of-the-art performance on VerifyBench-Hard and SCI-Bench. Furthermore, when employed as an RLVR reward model, CoSineVerifier consistently outperforms both rubric- and model-based verifiers on AIME’24, AIME’25 and GPQA-D, highlighting its potential to advance LLM reasoning and accelerate data processing

The unlearning method aims at effectively removing harmful, sensitive, or outdated knowledge without costly retraining the model. However, existing methods suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting, where aggressive unlearning degrades the model’s general capabilities. To address these challenges, we propose DirectiOn Guide unlEarning (DOGE), a novel method that enables precise knowledge erasure by identifying and leveraging a targeted “unlearning direction” in the model’s parameter space. DOGE first extracts this direction through differential analysis of representations for forgotten and retained samples, pinpointing the exact subspace associated with unwanted knowledge. It then selectively applies updates along this direction, ensuring minimal interference with retained information and general model performance. Experiments across multiple benchmarks demonstrate that Doge achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks–machine translation and dialogue response generation–as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.