5arXiv cs.CL (Computation and Language)·
LongCrafter: Evidence-graph-guided synthesis framework for long-context SFT data
LongCrafter is a structured framework for synthesizing long-context supervised fine-tuning data, addressing limitations of prior approaches including narrow task coverage, low difficulty, and lack of faithfulness supervision. The system uses a hierarchical 32-task taxonomy and constructs explicit evidence graphs modeling cross-paragraph dependencies to generate grounded instruction-response pairs. Models fine-tuned on LongCrafter data outperform SFT baselines and official post-trained models on LongBench, LongBench v2, and LooGLE for both Qwen2.5-7B and LLaMA-3.1-8B, with notable gains on high-difficulty tasks and improved robustness to the 'lost in the middle' problem.
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ReContext: Training-free recursive evidence replay improves LLM long-context reasoning
Researchers introduce RECONTEXT, a training-free inference-time method for improving long-context reasoning in LLMs. The approach uses model-internal relevance signals to build a query-conditioned evidence pool that is replayed before final generation, without modifying the original context, external memory, or context pruning. Experiments across eight long-context datasets at 128K context length show consistent improvements on Qwen3-4B, Qwen3-8B, and Llama3-8B. The authors provide a theoretical grounding via associative memory theory, framing attention as cue-trace association and replay as trace reactivation.
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.
QK-Restore: Fixing long-context recall degradation caused by CoT fine-tuning in hybrid LLMs
Researchers find that chain-of-thought supervised fine-tuning systematically degrades long-context recall in hybrid linear-attention models (HypeNet, Jet-Nemotron), with Needle-In-A-Haystack performance collapsing dramatically—e.g., HypeNet-9B dropping from 67.2% to 9.4% at 256K context. The root cause is identified as CoT-SFT biasing attention gradients toward short-range patterns, corrupting the query-key projections responsible for long-range routing. The paper proposes QK-Restore, a training-free fix that restores only W_Q and W_K from the pre-SFT checkpoint, recovering long-context capability while preserving reasoning gains.
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.
Weave of Formal Thought: Sound-and-complete constrained decoding with learned latent syntax for code LLMs
The paper introduces Weave of Formal Thought (WoFT), a framework combining a formally sound-and-complete constrained decoder for code generation with a latent-variable fine-tuning method that teaches LLMs to interleave grammar non-terminals during generation. The constrained decoder extends generalized LR (GLR) parsing with speculative lexing to handle context-sensitive lexing and maximal-munch tokenization, addressing gaps in prior constrained-decoding work. A reweighted wake-sleep (RWS) fine-tuning objective on StarCoder2-3B achieves a 14.3% relative reduction in per-token cross-entropy over a text-only SFT baseline on Python, suggesting that explicit structural scaffolding recovers information lost in flat autoregressive training.
Synthetic data generation method enables small LLMs to match large models on Text-To-Cypher tasks
A new arXiv paper presents an automatic synthetic data generation method for fine-tuning small LLMs on Text-To-Cypher (Text2Cypher) parsing, enabling natural language interfaces to property graph databases. Experiments across major Text-To-Cypher benchmarks show that small fine-tuned models can compete with much larger proprietary models. The approach is positioned as a solution for local deployment scenarios requiring data sovereignty without expensive annotation.
IS-CoT framework addresses long-form generation collapse in LLMs via interleaved structural thinking
Researchers introduce IS-CoT (Interleaved Structural Chain-of-Thought), a framework that embeds a dynamic Plan-Write-Reflect cycle into LLM generation to overcome severe length collapse observed in reasoning-enhanced models for open-ended writing tasks beyond 2,000 words. The authors construct a multi-teacher training dataset of interleaved reasoning traces and train IS-Writer-8B, which achieves state-of-the-art results on LongBench-Write, outperforming DeepSeek-V3.2 by 3.08 points. The work identifies static hierarchical planning as a root cause of long-form degradation and proposes an in-model alternative to external agentic workflows.
PROVE framework trains LLMs for multi-step tool use via stateful MCP environments and programmatic rewards
Researchers introduce PROVE (Programmatic Rewards On Verified Environments), a framework for training LLMs to orchestrate multi-step tool calls using reinforcement learning. The system includes a library of 20 stateful MCP servers with 343 tools, an automated data synthesis pipeline that grounds training queries in live server state, and a multi-component programmatic reward function requiring no judge model. Training four models (Qwen3-4B, Qwen3-8B, Qwen2.5-7B, Granite-4.1-8B) with ~13K examples yields gains of up to +10.2 on BFCL Multi-Turn, +6.8 on tau2-bench, and +6.5 on T-Eval, demonstrating consistent improvements in multi-step tool orchestration.