PhysioSeq2Seq combines patient-specific physiological digital twin modeling with Seq2Seq LSTM for 240-minute glucose forecasting in type 1 diabetes. Trained on 348 participants (T1DEXI), evaluated on 74: MAE 39.28 mg/dL at 240-min horizon, reducing bias by 13.89 mg/dL vs recursive LSTM.
Beacon is a diagnostic benchmark measuring sycophancy (LLMs' tendency to prioritize user agreement over factual accuracy) across 12 SOTA models. Authors identify stable linguistic and affective sub-biases scaling with model capacity, and propose prompt-level and activation-level interventions to modulate them.
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.
New AG-MG parallel corpus with 132,481 sentence pairs for Ancient-to-Modern Greek translation. Creation pipeline combines web-scraping, VecAlign alignment with fine-tuned LaBSE embeddings, and Gemini 2.5 Flash LLM-based correction. Benchmark of NMT models (NLLB, M2M100) and Greek LLM (Llama-Krikri-8B): full fine-tuning achieves 13.16 BLEU, gains up to +10.3 points.
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.
AgentKernelArena is an open-source benchmark for evaluating AI coding agents on GPU kernel optimization. It contains 196 tasks (HIP-to-HIP, Triton-to-Triton, PyTorch-to-HIP) and tests generalization to unseen configurations. Cursor Agent, Claude Code, and Codex Agent achieve speedups up to 6.89x, but PyTorch-to-HIP optimizations show correctness drops on unseen configurations.
Mosaic, a probabilistic weather forecasting model, addresses three spectral degradation failures in ML-based prediction: spectral damping, high-frequency aliasing, and residual leakage. With 214M parameters at 1.5° resolution, it matches models trained 6× finer and generates well-calibrated ensembles in 12s for 10-day forecasts on H100.
Causely is a causal intelligence layer for SRE workflows that structures environment topology and causal dependencies. Benchmark across 4 agent configurations (Claude Code, OpenAI Codex, HolmesGPT): with Causely, mean time-to-diagnosis reduced 63%, token consumption -60%, tool calls -78%, API cost per run -57%, root-cause accuracy 75%→100%.
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.
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.
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.
Comparative study of interpretability in Mixture-of-Experts (MoE) architectures vs dense networks. MoE experts exhibit lower neuronal polysemanticity than dense FFNs, especially with sparse routing. Experts function as fine-grained linguistic task specialists (e.g., closing LaTeX brackets), not broad domain specialists.
DSPR (Dual-Stream Physics-Residual Networks) proposes a forecasting framework that decouples stable temporal patterns from regime-dependent residual dynamics in industrial time series. Using an Adaptive Window module and Physics-Guided Dynamic Graph, it achieves 99% Mean Conservation Accuracy and 97.2% Total Variation Ratio across four industrial benchmarks.
Stream2LLM is an LLM serving system that reduces time-to-first-token (TTFT) by overlapping context retrieval with inference. It handles two modes: append (progressive accumulation) and update (iterative refinement). Evaluation on real workloads shows up to 11x TTFT improvement.
Systematic study of MoE model compression (Qwen3-Next-80A3B → 23A2B) via pruning and distillation at pretraining scale. Pruning outperforms training from scratch, multi-token prediction (MTP) distillation improves performance, and progressive schedules beat one-shot compression.
Training-free method to optimize long-context inference: attention-state memory externalizes prefix into lightweight lookup-based memory of precomputed attention states. On LLaMA-3.1-8B, improves in-context learning at 1K-8K tokens, reduces attention latency by 1.36x at 8K, outperforms full-attention RAG with 20% less memory.
PrivScope is an on-device payload governor enforcing task-scoped disclosure at the local-cloud boundary for hybrid agentic systems. On 100 medical-booking workflows, it eliminates profile leakage (0.0% vs 17.7%), halves attacker re-identification (23.1% vs 64.3%), and preserves task success without cloud-side changes across GPT-4o-mini and Gemini 2.5 Flash.
EPIC (Efficient Preference-aligned Index Construction) optimizes on-device RAG by storing user preferences instead of raw data. Across 4 benchmarks, reduces indexing memory by 2404×, improves preference-following accuracy by 20.17 percentage points, and lowers retrieval latency by 33.33×. Memory footprint under 1 MB with 29.35 ms/query.
Attractor-Vascular Coupling Theory (AVCT): mathematical framework showing cardiac attractor geometry encodes blood pressure information. Calibrated LightGBM model on smartphone PPG achieves MAE 2.05 mmHg (SBP) and 1.67 mmHg (DBP) in strict leave-one-subject-out cross-validation (46 subjects, 29,684 windows), meeting AAMI/IEEE SP10 criteria. PPG-only ablation matches ECG+PPG within 0.05 mmHg.
Mechanistic study of in-context learning (ICL): n-shot function vectors decompose linearly into individual example contributions. Models adaptively reweight demonstrations via attention, favoring informative and unambiguous examples. Query-Key alignment dominates function vector quality.
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.
Multilingual coreference resolution system based on Gemma-3-27b with two-stage adaptation (multilingual base adapters then dataset-specific adapters). CoNLL F1 score of 74.32 on CRAC 2026 test set, ranked first in LLM track. Mention spans represented by headword using XML-inspired format with local reindexing.
Wavelet Flow Matching (WFM) is a generative emulator for multi-scale physical systems governed by PDEs. It performs optimal transport directly in the hierarchical wavelet space of a U-Net, without pre-trained autoencoders. On three chaotic fluid dynamics systems, WFM outperforms SOTA models in long-horizon stability, accuracy, and spectral coherence.
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.
MixSD is an external-teacher-free fine-tuning method that injects knowledge by dynamically mixing tokens from two model conditionals: an expert branch observing the injected fact, and a naive branch reflecting original priors. On QA and knowledge-editing benchmarks, MixSD retains up to 100% of base model capabilities versus 1% for standard SFT.
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.
Charon is a unified, modular simulator for predicting large-scale LLM training and inference performance. It achieves prediction error under 5.35% overall and 3.74% for training on GPU clusters. In inference deployment, it identified a configuration improving system throughput over engineering-tuned baselines.
SPOT (Surgical Post-Training) is an on-policy distillation framework that injects reasoning capabilities into LLMs while preserving prior knowledge. With 4k rectified math pairs, it improves Qwen3-8B by 6.2% on average in 16 minutes on 8x H800, using KL-constrained reward formulation and minimal-edit error correction pipeline.
ProxyKV introduces a cross-model proxy pruning framework to accelerate long-context LLM inference. A lightweight Small-Model Proxy asynchronously scores KV cache importance for the target model. Tested on Llama-3.1, Qwen-2.5, and Qwen-3: recovers 98.7% of KVZip accuracy with up to 3.21× prefilling speedup (Llama-3.1-8B, dual-GPU) and sustains gains up to 170k tokens.
TopoPrior learns reusable collaboration structures for multi-agent LLM systems. Using a conditional variational graph framework, it captures structural regularities across domains offline, reducing online search cost and inference-time token consumption while remaining compatible with existing topology-evolution backbones.
LoRA-Over improves parameter-efficient fine-tuning (PEFT) by enriching the optimization landscape during training via auxiliary over-parameterization, then collapsing this enrichment into standard LoRA structure at inference. Evaluated on GLUE, MT-Bench, GSM8K, and HumanEval with LLaMA 2-7B and 3.1-8B, the framework consistently outperforms vanilla LoRA with no additional inference cost.
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.
New watermarking approach against unauthorized LLM distillation: behavioral markers (follow-up questions, low-frequency variants, restatements) injected via system prompt. Tested on 63 LoRA-distilled models from Llama-3.3-70B, with transfer rates 88.9% (Gemma) to 45.2% (Qwen). Robustness validated against DIPPER paraphrasing and user study (N=20) confirming imperceptibility.
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.
MacroDiff+ is a physics-guided geometric diffusion framework for macro placement optimization in VLSI physical design. Using a dual-domain architecture (heterogeneous GNNs + Transformer) and physics-guided gradient-based sampling, it achieves 6.1-6.2% wirelength reduction on ISPD2005 benchmarks with superior stability on large-scale designs.
MINE (Mechanistically Interpretable Neural Encoding) applies mechanistic interpretability to neural encoding models to identify visual features driving activation in individual voxels of human visual cortex. Using language-aligned image representations and counterfactual editing, the approach causally validates fine-grained selectivity in category-selective brain regions.
LEAP introduces end-to-end unstructured pruning for LLMs via per-weight Bernoulli-Gumbel-sigmoid relaxation. Across five model families (0.5B–8B) at 50–60% sparsity, LEAP improves average zero-shot accuracy by +2.59 points over ADMM baseline.
SCICONVBENCH benchmarks LLMs on multi-turn clarification of ill-posed scientific problems across fluid mechanics, solid mechanics, materials science, and PDEs. Best models resolve only 52.7% of disambiguation cases in fluid mechanics, but perform better on inconsistency detection. Evaluates clarification behavior, conversational grounding, and specification fidelity.
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.
Systematic optimization of real-time diffusion models on Apple M3 Ultra (60-core GPU, 512 GB unified memory). CoreML conversion of distilled SDXS-512 combined with 3-thread camera pipeline achieves 22.7 FPS at 512x512 resolution. Demonstrates that CUDA optimization insights don't transfer to Apple Silicon's unified memory architecture.