Entity · model
Qwen2.5-7B
modelactive
qwen2-5-7b-859ec241·10 events·first seen Aliases: Qwen2.5-7B, Qwen 2.5-14B, Qwen 2.5 14B, Qwen2.5 7B
Co-occurring entities
Llama-3.1-8BGRPOQwen3-4BCARE (Confidence-Adaptive Routing of Experts)Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRAHL7 FHIR R4Gemma 2 9BCPTICD-10LIARPolitiFactHippoRAGFEVERGRPO (Group Relative Policy Optimization)DeLIVeRSafeAgentBenchPhi-3.5Qwen2.5-3BEARBenchSmolLM2
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Recent events (10)
CARE: Confidence-Adaptive Routing for Mixture-of-Experts LoRA adjusts expert count per token
Researchers introduce CARE (Confidence-Adaptive Routing of Experts), a drop-in routing rule for MoE-LoRA that dynamically adjusts the number of active experts per token based on router output uncertainty rather than using a fixed top-k. The method uses nucleus-style cumulative mass thresholding with a budget thermostat to hit any target average expert count. Evaluated on LLaMA-3.1-8B and Qwen2.5-7B across commonsense, math, code, and knowledge benchmarks, CARE matches or outperforms fixed top-k baselines at equal compute while also improving out-of-distribution detection.
Closed-loop validation-repair achieves 99% schema compliance for clinical LLMs across healthcare standards
A new arXiv paper evaluates three open-source models (Qwen2.5 7B, Llama 3.1 8B, Gemma2 9B) on schema compliance with ICD-10, CPT, and HL7 FHIR standards across 960 clinical scenario-model pairs. Baseline compliance ranged from 85.9–91.6%, with 96% of failures being representation-level format violations rather than clinical reasoning errors. A closed-loop validation-repair framework raised overall compliance to 99.0%, with most errors resolving in one or two iterations, suggesting this system-level approach is a viable safeguard for healthcare EHR integration.
DeLIVeR: Reinforced knowledge graph exploration for LLM fact-checking
DeLIVeR is a new framework for automated fact-checking that decomposes complex claims into targeted questions and traverses structured Knowledge Graphs for evidence retrieval, optimizing a Planner LLM via Group Relative Policy Optimization (GRPO). Evaluated on LIAR, FEVER, and PolitiFact benchmarks using Qwen2.5-7B, the system achieves F1-scores of 83.73, 84.57, and 79.70 respectively, representing a 10-15% improvement over HippoRAG2. The approach addresses 'query brittleness' in traditional retrieval by framing evidence gathering as a reinforced strategic exploration task, yielding auditable reasoning paths.
PRISM probe separates physical danger from text-level safety in LLM hidden states for embodied agents
Researchers introduce PRISM, a single-layer logistic probe over LLM hidden states that distinguishes physically grounded danger (e.g., unsafe robot actions) from ordinary text-level content danger, showing these are separable signals in representations across multiple model families. PRISM achieves 86.2–87.7% accuracy on SafeAgentBench with substantially lower false-positive rates than same-scale LLM judges, which over-block safe tasks at 24.7–39.0% FPR. The authors also release PhysicalSafetyBench-1K (PSB-1K), a 1,000-pair contrastive benchmark for evaluating physical-risk detection without relying on explicit harm keywords. The work is relevant to safety in embodied AI and agentic systems where linguistic safety filters are insufficient.
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.
RL reward function design for LLM-generated BPMN process models: systematic study across 48 configurations
Researchers present a systematic study of reward function design for reinforcement learning applied to LLM-based BPMN process model generation, training Llama 3.1 8B and Qwen 2.5 14B across 48 configurations using Group Sequence Policy Optimization. Key findings: RL substantially improves syntactic and pragmatic quality while preserving semantic fidelity, equal reward weighting outperforms targeted weighting, and reward design effects interact with model architecture in non-trivial ways. The paper argues reward composition is as consequential as the decision to apply RL at all, with implications for any multi-dimensional structured generation task.
Supervised vs. in-context learning for Turkish multiword expression classification
A new arXiv paper evaluates Turkish idiomatic light verb construction (LVC) detection as a binary classification task, comparing a supervised BERTurk baseline against three instruction-tuned LLMs under zero-shot, one-shot, and few-shot prompting. Results show LLMs have very low LVC recall in zero-shot but improve substantially with demonstrations, though one-shot prompting can introduce strong model-specific biases. The supervised baseline remains competitive, while carefully constructed few-shot prompts allow GPT-OSS-20B and Qwen 2.5-14B to match or exceed it. The study highlights significant prompt sensitivity in Turkish metalinguistic classification tasks.
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.
Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA
This paper investigates why NLI-based claim checkers used as process rewards in RL-trained medical RAG agents succeed or fail during training. The authors find that a checker's output distribution during training—not its held-out accuracy—determines whether it provides useful gradient signal, with LLM log-probability scoring causing near-total signal collapse (97%+ neutral labels) while a calibrated MedNLI classifier avoids this. A key finding is that stronger checkers can trigger reward hacking cascades (ultra-short answers, search avoidance, language collapse), while moderate-signal local classifiers yield better final model quality (+12% BERTScore over zero-shot). The work frames these as boundary conditions for verifier-as-reward systems in RLVR pipelines.
The Matching Principle: A Geometric Theory Unifying Robustness, Domain Adaptation, and Alignment via Nuisance Covariance
This paper proposes the 'matching principle': a unified geometric framework arguing that robustness methods (CORAL, IRM, adversarial training, augmentation, metric learning, Jacobian penalties, alignment constraints) are all estimators of the same object—the covariance of label-preserving deployment nuisance—and that regularizing the encoder Jacobian along this covariance's range is the core statistical problem. The authors prove closed-form optimality results in a linear-Gaussian model, introduce the Trajectory Deviation Index (TDI) as a label-free embedding sensitivity probe, and validate predictions across 13 pre-registered experimental blocks including Qwen2.5-7B. At 7B scale, matched style-PMH improves selective honesty while standard DPO degrades Style TDI, connecting the theory to alignment safety.