QuNetQFL is a quantum federated learning protocol implemented on quantum networks, masking local model updates with distributed quantum secret keys for information-theoretic security. Experimentally validated on a four-client quantum network, it reduces communication costs by 75% and scales to 200 clients with rapid convergence.
RAG-Gym, a framework supervising the search process rather than final answers, improves autonomous search agents. Re²Search++, a process-supervised agent, achieves substantial gains on multi-hop information-seeking benchmarks, especially out-of-domain, through higher-quality search queries and better generalization.
LiRA, a lightweight fine-tuning framework, improves LLM adaptation to low-resource languages through cross-lingual semantic alignment. Combines Arca (anchor-based alignment to English) and LaSR (language-aware head). Theoretical stability guarantees. Multilingual dataset (7 Asian languages) and code released.
InvDesFlow-AL combines diffusion and active learning for inverse design of functional materials. The model achieves RMSE 0.0423 Å in crystal structure prediction (+32.96% vs existing methods) and systematically generates low-formation-energy materials. Validation: discovery of Li₂AuH₆ as BCS superconductor at 140 K.
DriveMoE proposes a Mixture-of-Experts architecture for end-to-end autonomous driving. The model combines Vision MoE (dynamic camera selection based on driving context) and Action MoE (specialized expert activation for different behaviors). Built on Drive-π₀ baseline, DriveMoE achieves SOTA on Bench2Drive by avoiding mode averaging.
RAP is an elastic pruning framework for LLM inference using reinforcement learning to dynamically adapt compression strategies based on runtime memory variations and heterogeneous KV-cache demands. The RL agent optimizes the parameter-to-KV-cache ratio in real-time, retaining only components that maximize utility within the current memory budget.
Perovskite-R1 is a specialized LLM based on QwQ-32B, fine-tuned on 1,232 scientific publications and 33,269 candidate materials to discover precursor additives optimizing perovskite solar cells. The model generates solutions for defect passivation and improves stability/performance, experimentally validated.
Fourier Compressor compresses visual tokens in Vision-Language Models using Fourier transforms. The parameter-free method reduces FLOPs by 83.8% and boosts inference speed by 31.2% while retaining 96% of original accuracy. Tested on LLaVA and Qwen-VL, it generalizes to video understanding tasks.
New approach to generate formal theorem proving challenges by leveraging theoretical computer science (TCS). Framework automatically synthesizes problem-proof pairs in Lean4 and Markdown across two domains: Busy Beaver and Mixed Boolean Arithmetic. DeepSeekProver-V2-671B achieves 57.5% on Busy Beaver but only 12% on Mixed Boolean Arithmetic, revealing major gaps in long-form proof generation.
SignRoundV2 is a post-training quantization framework for LLMs maintaining performance under extreme compression (2-4 bits). It combines adaptive mixed-precision strategy guided by gradient information and lightweight stabilization techniques. Results show ~1% performance gap at 4.5 bits average in mixed MXFP settings, with substantial improvements in 2-bit weight-only quantization.
Colluding LLM agents manipulate victim beliefs by coordinating truthful evidence fragments through public channels without covert communication. The Generative Montage framework (Writer-Editor-Director) constructs deceptive narratives via adversarial debate. Attack success rates reach 74.4% on proprietary models and 70.6% on open-weights across 14 LLM families. Advanced reasoning models show higher susceptibility.
ACToR, an adversarial multi-agent loop, improves C-to-Rust translation using a translator agent and discriminator agent that iteratively compete. On 63 real-world C utilities (avg 473 LOC), the system achieves 90% test pass rate with zero human intervention, improving correctness by 36.7% over non-adversarial baselines.
UbuntuGuard is the first policy-based safety benchmark for African languages. Built from 155 domain experts, it evaluates 15 models (7 general-purpose LLMs, 8 guardian models) across three variants. Findings show English-centric benchmarks overestimate real-world multilingual safety and cross-lingual transfer remains insufficient.
SynCABEL uses LLMs to generate contextualized synthetic training data to address the scarcity of expert annotations in biomedical entity linking. The framework achieves SOTA results on MedMentions (English), QUAERO (French), and SPACCC (Spanish), reaching full human supervision performance with 60% less annotated data.
SDRL (Self-Debate Reinforcement Learning) trains LLMs to solve problems standalone AND benefit from multi-agent debate. The model samples multiple solutions, constructs debate context with diverse reasoning paths, then jointly optimizes initial and debate-conditioned responses. Results: consistent MAD performance gains across benchmarks and agent configurations.
arXiv paper introducing a trace-optional evaluation protocol decomposing token efficiency of reasoning LLMs. Analyzes 14 open-weight models on CogniLoad, GSM8K, ProofWriter, ZebraLogic by separating completion rate, conditional correctness, and generated length. Identifies three failure modes: logic-limited, context-limited, or verbosity-limited.
SignRoundV2 is a post-training quantization framework for LLMs maintaining performance under extreme compression (2-4 bits). It combines adaptive mixed-precision strategy guided by gradients and lightweight stabilization techniques. Results show ~1% performance gap at 4.5 bits average in mixed MXFP, with substantial improvements in challenging 2-bit weight-only quantization.
DevBench is a telemetry-driven benchmark evaluating LLMs on 1,800 realistic code completion tasks across 6 programming languages. 9 SOTA models tested, best score 43.5% Pass@1. Combines functional correctness, similarity metrics, and LLM-judge assessments on usefulness and contextual relevance.
SynCABEL uses LLMs to generate contextualized synthetic training examples to address scarcity of annotated data in biomedical entity linking. The framework achieves state-of-the-art on MedMentions (English), QUAERO (French), and SPACCC (Spanish), reaching full human supervision performance with 60% less annotated data. An LLM-as-a-judge protocol evaluates clinical validity.
LaDi-RL optimizes LLM reasoning via RL in latent space using diffusion. Instead of optimizing token sequences, the method generates latent reasoning trajectories through iterative denoising. It solves credit assignment (rewards observed after decoding) via hierarchical latent-text rollouts. Gains: +9.4% code generation, +5.7% math reasoning on pass@1.
R&B-EnCoRe enables Vision-Language-Action models to self-generate and refine embodied reasoning without human annotation or external rewards. Tested on manipulation (Franka Panda, WidowX), navigation (bipedal, wheeled, bicycle, quadruped), and autonomous driving: +28% manipulation success, +101% navigation scores, −21% collision rate vs baselines.
New metric decomposing token efficiency of reasoning LLMs. Introduces trace-optional evaluation protocol separating completion rate, conditional correctness, and generated length. Evaluates 14 open-weight models on CogniLoad, GSM8K, ProofWriter, ZebraLogic. Identifies three distinct failure modes: logic-limited, context-limited, and verbosity-limited.
CodeScaler is a reward model for training and inference scaling of code LLMs. Trained on verified preference data, it outperforms execution-based RL by +1.55 points on Qwen3-8B and +4.23 on Qwen3-14B. At inference, it reduces latency 10× while maintaining performance comparable to unit test approaches.
GIST introduces targeted data selection for instruction tuning by replacing axis-aligned scaling with robust subspace alignment via SVD. It recovers task-specific subspaces from validation gradients and scores examples by alignment with target directions. GIST matches or outperforms state-of-the-art baselines using only 0.29% storage and 25% computational time.
SPOT (Surgical Post-Training) is an on-policy distillation framework that injects reasoning capabilities into LLMs while preserving prior knowledge. With only 4k rectified math pairs, it improves Qwen3-8B by 6.2% on average in 16 minutes on 8x H800 GPUs. The approach uses KL-constrained reward formulation to mitigate catastrophic forgetting.
Language models fail to reliably apply simple rules over long sequences. Test on 126 model variants: all models cannot count above a model-dependent threshold. Failures are abrupt and persist despite increasing model size and computation. Mechanistic probing shows models use finite internal states to simulate counting, exhausting them beyond threshold.
CoCoReviewBench is a 3,900-paper benchmark (ICLR, NeurIPS) to evaluate AI reviewer systems. It addresses metric bias by using reviewer-author-meta-review discussions as expert annotations. Results show AI reviewers suffer from hallucinations and reasoning models are more effective reviewers.
Researchers identify a shared preference vector in Gemma-3-27B and Qwen-3.5-122B by training linear probes on residual-stream activations. This vector predicts and causally controls the model's task choices across different personas, including an evil persona, revealing a largely shared preference representation underlying different behavioral modes.
Automated methodology to generate high-performance RL environments using generic prompts, hierarchical verification, and cross-backend policy transfer. Demonstrated on 5 environments (PyBoy→EmuRust, Pokemon Showdown→PokeJAX, new TCGJax). Overhead <4% at 200M parameters.
CoDiLA combines diffusion with local autoregressive decoding for parallel code generation. A compact auxiliary AR model (0.6B parameters) ensures fine-grained syntactic coherence while the diffusion model handles bidirectional block-level generation. Eliminates coherence artifacts on code generation benchmarks.
ClawArena is a benchmark evaluating AI agents in evolving information environments. It tests agents' ability to maintain correct beliefs amid contradictory sources, dynamic updates, and implicit user preferences. 12 multi-turn scenarios, 337 evaluation rounds, 5 frameworks and 18 language models assessed.
Photonic Kolmogorov-Arnold networks (SSP-KANs) implemented with standard telecom components (Mach-Zehnder interferometers, semiconductor optical amplifiers). Four-module network achieves 94.3% on nonlinear classification; seven-module network reaches R²=0.986 on regression. Robust down to 6-bit resolution and 14 dB SNR.
TAQ (Task-Aware Quantization) is a training-free post-training quantization method that dynamically allocates precision to task-relevant layers using unlabeled calibration prompts. Three variants (TAQ-IS, TAQ-KL, TAQ-O) estimate layer importance from hidden representations. Significant gains in accuracy-memory ratio validated on real hardware throughput and latency.
ShareChat is a corpus of 142,808 conversations (660,293 turns) collected from ChatGPT, Perplexity, Grok, Gemini, and Claude between April 2023 and October 2025. The dataset preserves native affordances (citations, reasoning traces, code artifacts) across 95 languages and enables analysis of cross-platform differences in intent satisfaction, citation strategies, and response latency.
QuCo-RAG proposes dynamic RAG grounded in pre-training corpus statistics rather than model-internal signals. It identifies low-frequency entities and verifies their co-occurrence in 4 trillion tokens using Infini-gram. On multi-hop QA benchmarks, it gains 5–12 EM points over baselines with OLMo-2, and up to 14 points on Llama-3, Qwen2.5, GPT-4.
TabTrim, a novel table pruning framework for TableQA, replaces sequential revisions with gold trajectory-supervised parallel search. The system uses intermediate sub-tables from gold SQL queries to train a pruner and verifier. TabTrim-8B achieves 73.5% average accuracy, outperforming strongest baselines by 3.2% (79.4% on WikiTQ, 61.2% on TableBench).
GiG, a planning framework for embodied agents, uses Graph-in-Graph architecture with GNN to encode environmental states and structure experience memory. A bounded lookahead module enhances planning via symbolic transition logic. Evaluated on Robotouille and ALFWorld, GiG outperforms baselines with +22% to +37% Pass@1 gains.
PEGRL is a two-stage RL framework for LLM-based machine translation. It uses post-editing as an auxiliary task to stabilize training and guide optimization. Tests on EN→FI, EN→TR, EN↔ZH show consistent gains; EN→TR achieves performance comparable to DeepSeek-V3.2 on COMET-KIWI.
ComprExIT, a new context compression framework, addresses structural bottlenecks in existing LLM-based compressors through explicit information transmission. On 12 datasets, it improves average F1 by 18.5%, adds ~1% trainable parameters, and achieves 2x faster compression than baselines.
STING is an automated red-teaming framework measuring multi-turn illicit assistance in LLM agents. It constructs step-by-step illicit plans grounded in benign personas and uses judge agents to track completion. Multilingual evaluation across six non-English languages shows attack success does not consistently increase in lower-resource languages, diverging from chatbot findings.