Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection
Loong is a long document translation agent that uses a 3E memory module (Essence-Exemplar-Entity) to store structured historical context, replacing passive full-context attention with RL-optimized adaptive context selection. The agent learns its context retrieval policy via reinforcement learning on self-sampled reasoning trajectories. Evaluations show average gains of up to 13.0 points across three metrics in English↔Chinese, German, and French translation directions, with strong generalization and robustness to noise in ultra-long documents.
Related guides (3)
Related events (8)
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
Alibaba's Qwen team describes an agent built on Qwen2 (8k native context) that processes documents up to 1M tokens by decomposing retrieval and reasoning tasks, reportedly outperforming both RAG pipelines and native long-context models. The agent framework was also used to generate synthetic training data for fine-tuning new long-context Qwen models, creating a self-improvement loop. This positions agent-based context extension as a practical alternative to architectural long-context training.
Luar: Selective Translation via Reinforcement Learning for Multilingual Reasoning
Luar is a reinforcement learning framework that trains reasoning language models to selectively invoke English translation only when direct understanding of a non-English input is deemed unreliable. The approach, built on top of GRPO, outperforms standard multilingual baselines across reasoning benchmarks, with especially large gains on low-resource languages. Analysis confirms the model learns to avoid unnecessary translation when direct reasoning suffices, and generalizes the translation-call behavior to unseen low-resource languages.
DOA: Training-Free Decoder-Only Attention Policy for Long-Form Simultaneous Speech Translation with SpeechLLMs
The paper proposes Decoder-Only Attention (DOA), a training-free streaming policy for simultaneous speech-to-text translation (SimulST) that works with off-the-shelf decoder-only Speech LLMs. DOA derives proxy alignment signals from self-attention rather than cross-attention, enabling long-form simultaneous translation without retraining. Experiments on Phi4-Multimodal and Qwen3-Omni demonstrate low-latency performance approaching offline decoding quality, validating that decoder self-attention contains sufficient alignment information for streaming decisions.
ContextRL: Context-aware reinforcement learning improves grounding in agentic and multimodal LLMs
Researchers introduce ContextRL, a reinforcement learning method that trains LLMs to select the context that supports a given query-answer pair from two highly similar candidates, rather than supervising only final answers. The approach constructs contrastive context pairs in two domains: coding agent trajectories (1k pairs) and multimodal image pairs (7k pairs). ContextRL achieves +2.2% average gains over standard GRPO on 5 long-horizon benchmarks and +1.8% across 12 visual QA benchmarks, with ablations showing the gains stem from the context-selection objective rather than the contrastive data alone.
LANG: Reinforcement Learning Framework for Multilingual Reasoning with Language-Adaptive Hint Guidance
LANG is a new RL-based framework for improving multilingual reasoning in LLMs that addresses the trade-off between input-language consistency and reasoning quality. It uses language-conditioned hints with a progressive decay schedule and a language-adaptive switch to tailor learning to per-language difficulty. Empirical results on multilingual mathematical benchmarks show improved reasoning without language drift toward English, and the approach generalizes beyond mathematics.
OmniAgent: POMDP-based active perception agent for long video understanding with test-time scaling
Researchers introduce OmniAgent, a multimodal agent that reformulates long video understanding as a POMDP-based iterative Observation-Thought-Action cycle, selectively distilling audio-visual cues into persistent textual memory rather than processing all frames uniformly. The system uses Agentic Supervised Fine-Tuning and a novel reinforcement learning method (TAURA) with turn-level entropy for credit assignment. OmniAgent demonstrates positive test-time scaling and achieves state-of-the-art open-source results across ten benchmarks, with its 7B model outperforming Qwen2.5-VL-72B on LVBench (50.5% vs. 47.3%).
LongTraceRL: Reinforcement Learning for Long-Context Reasoning via Search Agent Trajectories and Rubric Rewards
LongTraceRL is a new RL training framework for improving long-context reasoning in LLMs, addressing limitations of existing RLVR methods. It constructs challenging training data using multi-hop questions from knowledge graph random walks and tiered distractors derived from search agent trajectories (high-confusability: read but uncited; low-confusability: seen but unopened). A rubric reward provides entity-level process supervision along reasoning chains, applied only to correct responses to prevent reward hacking. Experiments across three LLMs (4B–30B parameters) on five long-context benchmarks show consistent improvements over strong baselines.
AgentCL: A Rigorous Evaluation Framework for Continual Learning in Language Agents
AgentCL is a new benchmark and evaluation framework designed to rigorously assess continual learning in language agents, addressing gaps in existing benchmarks that focus on retrieval over long-context documents or use naive task streams with limited cross-task analysis. The framework constructs compositional task streams where earlier sub-solutions, evidence, or workflows are intentionally reusable in later tasks, contrasting them with naive streams to measure transfer gains. The authors also introduce MemProbe, a probing method that stores interactions, insights, and skills while filtering unreliable experiences during consolidation. Empirical results across coding, deep research, and language understanding tasks show that controlled streams better distinguish memory design quality, and that naive streams can mask memory-induced degradation.