Contagion Networks introduces a framework to measure how evaluator biases spread among LLM agents. In a 3-agent experiment, biases propagated consistently with contagion coefficients between 0.157 and 0.352, and homogeneous-model agents showed significantly weaker contagion than cross-model setups. Increasing evaluator committee size from k=1 to k=3 reduced effective contagion by 72.4%.
CWE-Trace evaluates eight vanilla and 15 LoRA-fine-tuned LLMs on Linux kernel vulnerability detection. Results show data contamination offers no advantage, and fine-tuning only shifts output thresholds without altering decision policies. Despite improved detection scores, LLMs lack reliable security reasoning, with top-1 CWE accuracy below 1.3% and binary detection performance at 52.1%.
Contagion Networks introduces a framework to measure how evaluator biases spread among LLM agents. In a 3-agent experiment, biases propagate with coefficients between 0.157 and 0.352, and homogeneous-model agents show significantly weaker contagion than cross-model setups. Increasing evaluator committee size from k=1 to k=3 reduces effective contagion by 72.4%.
This work shows that Lean can serve as a symbolic process oracle, providing fine-grained, verified feedback during reinforcement learning. By parsing proof attempts into tactic sequences and using Lean's elaboration to mark sound steps and first failures, the system generates dense, type-theoretic reward signals. Experiments demonstrate tactic-level supervision outperforms outcome-only methods on benchmarks like MiniF2F and ProofNet, highlighting Lean's role as both evaluator and training reward source.
Diffusion-Proof is the first framework to train and apply diffusion language models for formal theorem proving. It introduces dLLM-Prover-7B for whole-proof writing with long-range coherence and dLLM-Corrector-7- for local proof correction using bidirectional information. The framework outperforms auto-regressive LLM baselines by 1.61% on ProofNet-Test and 6.14% on MiniF2F-Test, and solves an IMO problem beyond the capability of DeepSeek-Prover-V2-7B.
Spotlight enables DiT RL post-training by leveraging idle spot GPUs, reducing costs by 1.4-6.4x while achieving superior image quality. It uses stale model weights in exploration and reconfigures sequence parallelism on-the-fly, allowing efficient GPU utilization without breaking training pipelines.
SenFlow introduces a novel method for detecting AI-generated text in hybrid documents by modeling inter-sentence dependencies. It achieves state-of-the-art performance on MOSAIC, a benchmark of 16,000 documents from PubMed and XSum, with a +4.15 pp Macro-F1 gain on cross-domain transfer. SenFlow reveals that AI-generated content still exhibits generator-dependent sentence-length patterns, exploitable by sentence-level detectors despite perplexity filtering.
Spotlight enables DiT RL post-training by leveraging idle spot GPUs, reducing costs by 1.4-6.4× while achieving superior image quality. It uses stale model weights in exploration and reconfigures sequence parallelism in real time, allowing efficient GPU utilization without breaking training pipelines.
TAC, the first agentic benchmark for implicit animal welfare, tests AI agents' ability to avoid animal exploitation in travel booking scenarios. All seven frontier models score below 64%, with the best at 53%, and even minor prompt improvements yield only modest gains. An audit finds no signs of evaluation awareness, indicating performance gaps stem from lack of true welfare reasoning, not prompt recognition.
TAC evaluates whether AI agents avoid animal exploitation in travel bookings. Seven frontier models all score below 64% chance level, with Claude Opus 4.7 at 53%. Adding a welfare-aware system prompt improves performance significantly, though models show no evidence of evaluation awareness in their responses.
A study challenges the assumption that visual attention signals reliability in vision-language models. It finds near-zero correlation between spatial attention and accuracy, showing instead that self-consistency across reasoning paths is a stronger predictor of truth. Reliability is better explained by generation dynamics and internal state distributions, not visual attention patterns.
OPD-Evolver introduces a slow-fast co-evolution framework that enables agents to select, act on, and reuse experience through on-policy self-distillation. It outperforms existing memory and training-based methods by up to 11.5% and 5.8% respectively, and demonstrates capability to challenge large-scale models like Qwen3.5-397B-A17B and Step-3.5-Flash.
HalBench evaluates 29 open-source LLMs on a custom benchmark for sycophancy and hallucination. Qwen 3.6 and Gemma 4 outperform larger models, with Qwen 3.6 achieving 36.6% pushback—higher than GPT-5.4 and Gemini 3.1 Pro. Model size does not correlate with honest responses, indicating that architecture and training data matter more than parameters.
A new Text2DSL system uses context-aware distillation with a structured context of BNF grammar, API specification, and closed identifier vocabulary. Ablation studies show that the vocabulary has the largest impact on semantic quality, while API and BNF significantly improve structural validity, confirming structured context as a critical, load-bearing component.
Baidu has released Unlimited-OCR, a model that transcribes dozens of pages in a single forward pass using Reference Sliding Window Attention (R-SWA). It builds on DeepSeek-OCR, inheriting its encoder, image compression, and MoE architecture, with only 500M active parameters per token. The model achieves 93.92% accuracy on OmniDocBench v1.6, outperforming DeepSeek-OCR's 87.01% on v1.5, though vendor-reported results warrant independent validation.
A benchmark of 240 Arabic test items across eight domains and ten skills assesses twelve small language models in zero-shot settings. Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic, with performance linked more to Arabic alignment and instruction-following than model size. Common failure modes include prompt leakage, hallucination, and weak task adherence.
A detailed analysis maps the KLD (Kullback-Leibler divergence) of KV cache quantization for Qwen3.6-35B-A3B and Gemma4-E2B models. Results show q8/q8 quantization is nearly lossless on both models, while q4/q4 performs well on Qwen but causes severe degradation on Gemma. Turbo quantization variants show mixed performance, with turbo3 and turbo2 enabling extreme cache compression at significant accuracy cost.
A new Text2DSL system uses context-aware distillation with a structured context of BNF grammar, API specification, and closed identifier vocabulary. Ablation studies show that the vocabulary has the largest impact on semantic quality, while API and BNF significantly improve structural validity, confirming structured context as a critical, not superficial, component.
A benchmark of 240 Arabic test items across eight domains and ten skills assesses twelve small language models in zero-shot settings. Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic, with performance linked more to Arabic alignment and instruction-following than model size. Common failure modes include prompt leakage, hallucination, and weak task adherence.
DeepSeek has raised $7.4 billion in funding at a $60 billion valuation. Liang Wenfeng, the company's founder, personally invested $3 billion in the round, underscoring his significant stake and commitment to the company's growth.