Study of 403 US hyperscale data centers (May 2024–April 2025): estimated consumption 68–99 TWh, emissions 37–54 Mt CO2. Represent 1.8% of US electricity consumption. Carbon intensity 545 gCO2/kWh, 48% above national average (370 gCO2/kWh). 54% of electricity from fossil-fuel sources.
LLM judges used to evaluate AI models are unstable under post-decision interaction. On MT-Bench and AlpacaEval, researchers show initial judgments can be reversed through targeted challenges, degrading agreement with human preferences and shifting benchmark rankings. They introduce the Evaluation Robustness Score (ERS) to measure this fragility.
PACT is a protocol for inter-agent communication that compresses messages between LLM agents into compact action-state records. Tested on two MAS topologies, it reduces token usage by 10-50% while maintaining or improving performance on OpenHands and SWE-agent.
Comparison of LLMs for generating formal proofs in Lean 4. Gemini 3.1 Pro and Claude Opus 4.7 achieve best performance (92% and 86% success rates respectively via refine@32). NVIDIA Nemotron 3 Super and GPT-OSS 120B offer best cost-efficiency (<$0.01 per correct proof).
Novel multilingual fine-tuning method using multi-objective optimization (MOO) applied locally on parameter buckets. Resolves gradient conflicts across languages without communication overhead. Demonstrates improved performance on seen and unseen languages across 4 base LLMs.
arXiv study on LLM-driven program mutation: mutation chains converge rapidly toward restricted regions of program space. 87% of chains revisit 93% of previously seen structural forms. Phenomenon is robust across models and prompts, revealing systematic bias toward structural homogeneity incompatible with open-ended exploration.
Controlled intervention study shows RAG rewriting gains are driven by gold answer presence in rewritten context, not curation quality. Tests across Qwen2.5/3.5, GLM-4 and HotpotQA/2WikiMultihopQA: removing answer drops F1 by 28–64 points, injecting it raises F1 by +0.7 to +9.7 points. Authors release intervention runner and sentinel panel for reproducible evaluation.
RedNote (Xiaohongshu) releases dots.tts, an open-source TTS model with 2B parameters under Apache 2.0. Fully continuous architecture without codec tokens, 48 kHz synthesis, zero-shot voice cloning, direct text-to-speech pipeline.
CPU inference benchmark for Parakeet TDT 0.6B on 2 x86-64 vCPUs (7.7GB RAM). ONNX Runtime FP32 achieves RTF 0.328 (37% faster than HF Transformers bfloat16 at 0.519) but peaks at 2.7GB memory. GGUF Q6_K reduces to 928MB but doubles RTF to 0.708. Methodological note: espeak-ng inflates WER to 20.9% vs gTTS 4.65%.
AdaPlanBench is an interactive benchmark evaluating LLM agents' ability to adaptively plan and replan under progressively revealed world and user constraints. Built on 307 household tasks, it tests 10 leading models: best achieves 67.75% accuracy. Performance degrades with constraint accumulation, particularly for user constraints.
arXiv paper demonstrating that optimal hyperparameters for LLM continued pre-training follow predictable scaling laws. Two-stage framework: empirical law discovery via proxy models, then state-aware prediction using validation loss and equivalent pre-training compute. Reduces hyperparameter search overhead by 90% while maintaining performance.
A4D introduces a functional latent space organized around object affordances rather than appearance to improve robot planning generalization. The system achieves 94% accuracy on existing affordances (+15 points vs SOTA) and 90% on new affordances using only 10% of original training data, with 100x faster inference.
PRIG, a gradient attribution method, localizes ambiguity in LLM prompts by training a linear probe to distinguish clear from ambiguous prompts, then attributes the probe score to token representations. Evaluated on synthetic datasets (coding, math, writing) and a human-written gold benchmark, PRIG achieves 0.840 AUROC on combined synthetic benchmark and 0.891 AUROC on gold set.
LEVANTE-bench benchmarks vision-language models against children aged 5-12 (N=1547) on cognitive tasks across 3 countries. Larger models show better overall alignment, but smaller models match younger children's error patterns better. VLMs struggle on matrix reasoning and mental rotation tasks.
SHALA-LLM is a reinforcement learning framework that treats label ambiguity as useful information rather than noise. On NLI and emotion recognition tasks, it reduces Jensen-Shannon Distance by 62.1% on ChaosNLI and improves F1 by 16.7% by learning directly from annotator distributions.
Agentic Monte Carlo (AMC) optimizes black-box LLM agents without parameter access. The method uses Sequential Monte Carlo to sample from the optimal policy by learning a value function to steer the agent, leaving the underlying model unchanged. Validated on AgentGym, AMC outperforms prompting baselines and GRPO.
Epidemiological study of model collapse from synthetic data training. Bilayer SIR/SIRS framework models cross-contamination between data corpora and AI models. GPT-2 experiments on WikiText and Shakespeare (192 runs) confirm dose-response degradation; R₀ > 1 indicates supercritical dynamics. Synthetic-text detection and filtering identified as highest-leverage interventions.
TensorBench is a benchmark of 199 coding tasks (feature additions and refactoring) on an open-source compiler-based tensor framework extending PyTorch. Evaluation of 7 coding agents: pass rates from 64.8% (strongest) to 22.1% (weakest), with low inter-agent agreement (κ=0.05 for top two agents).
ArcANE is an automatically constructed benchmark evaluating whether role-playing language agents maintain character psychological consistency across narrative phases. Built on 17 novels and 80 principal characters, it tests responses across story phases and unseen scenarios. Conditioning on Character Arc outperforms all other context strategies across 6 models and 6 context modes.
BUMP is a self-supervised framework generating textual user profiles for LLM personalization. Trained via GRPO on bidirectional ranking objectives (profile vs interactions, interactions vs profiles), it requires no labeled supervision. Evaluated on LaMP, BUMP matches or outperforms closed-source APIs without task labels.
Fisher-MoE proposes compressing Mixture-of-Experts models by targeting intermediate FFN dimensions rather than entire experts. On Qwen1.5-MoE, removing just 12 of 1.35M critical dimensions (identified via Fisher importance) preserves performance while reducing memory by ~45% and improving inference throughput by 21%.
Stereological theory of LLM benchmark coverage. For d_eff ∈ [2.86, 4.80], structural blind spot exceeds runner-up score gap by two orders of magnitude. Submodular greedy algorithm identifies 4 stable benchmarks; 7 of 12 suffice for 90% coverage. Validation across 12 internal benchmarks and 27 Chatbot Arena categories.
Mechanistic cross-architecture study on 3 1B-class models (Pythia, OLMo, OLMoE) testing whether circuit identification via pattern selectivity + causal ablation yields reproducible findings. Result: same task, same behavioral capability, different implementations across models. Five-category taxonomy (primary cause, secondary cause, correlate, interferer, null) with quantitative thresholds introduced.
Multimarginal flow matching with optimal transport potentials. Novel approach combining flow matching and dynamic optimal transport to model temporal evolution with observed intermediate marginals. Simulation-free algorithm (OTP-FM) validated on RNA-seq, oceanographic, and meteorological datasets.
PRECISE extends Prediction-Powered Inference for ranking evaluation by combining small human-labeled sets with large LLM-judged sets using Claude 3 Sonnet. Reduces Precision@4 standard error from 4.45 to 3.50 (−21% relative). In production, correctly identifies best system variant from 100 human labels; A/B testing confirms +407 bps daily sales lift.
New training method to distinguish temporary computation from persistent state in language models. Counterfactual Erasure RL (CERL) rewards models when answers remain correct after erasing intermediate thoughts. Evaluation on mathematics, logic, and scientific QA shows reduced dependence on hidden computations without accuracy loss.
proveKV: open-source KV-cache compression technique for LLMs. Results: 36× lossless memory reduction vs f32, 18× vs fp16 on SmolLM2-1.7B + WikiText-2 (0% PPL regression). Automated audit pipeline with reproducible validation.
cyankiwi releases AWQ 4-bit update with NVFP4 and FP8 Dynamic quantization support. KL divergence benchmarks on Qwen3.6 27B and 35B-A3B: cyankiwi/Qwen3.6-27B-AWQ-INT4 achieves 0.020443 KLD (best dense), cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit achieves 0.017126 KLD (best MoE).
Agentcodec unifies 28 LLM reliability techniques (retries, ensembling, verification, adaptive routing) under a single API. Adoption via one-line import change. On Nemotron + Devstral + GLM-5.1, adaptive router achieves 56% cost reduction at matched quality, or 7% quality gain at matched cost. Single λ parameter controls the trade-off.
Detailed benchmark of Qwen3.6-27B on 2x RTX 3090 comparing llama.cpp (Q6_K/Q8_0) and vLLM (INT4/INT8). Real measurements: throughput 43-54 tok/s, MTP acceptance rates 27-77% per backend. Setup with OpenAI-compatible proxy hot-swapping 4 configs, no PCIe P2P (Threadripper 1950X).
Huawei open-sources KVarN, a KV-cache quantization method (Apache 2.0, vLLM single-flag integration). 3–5× compression vs FP16, throughput up to 1.4× FP16, maintains reasoning quality unlike TurboQuant (Google). No retraining, no calibration required.
Systematic study of spectral behavior in Muon, an orthonormalization-based optimizer using Newton-Schulz iteration. Across 77M-2.8B parameter models, singular value quantiles of momentum buffers stabilize following power laws dependent on layer depth (exponents M^-0.25 to M^-0.96). Implications for NS configuration at frontier scale.
Training-free lexical-dense fusion study for long-term conversational memory retrieval. Score-level fusion of late-interaction dense + BM25 improves Hit@1 by +8.8 to +17.2 points across six encoders (Hit@1 0.752 with e5-large-v2). Web search cross-encoder reranker degrades results (-6.9 pp). Analysis shows division of labor: dense excels on multi-hop/temporal questions, BM25 on adversarial ones.
Novel framework for learning admissible heuristics in optimal planning via cost partitioning. A neural network with axial self-attention predicts cost weights guaranteed admissible by construction, leveraging Lagrangian dual equivalence. Results show reduced node expansions versus suboptimal baselines while preserving optimality.
TMEM introduces a self-evolving parametric memory framework for LLM agents. Instead of storing experience solely as textual context, the agent absorbs distilled supervision into lightweight LoRA weights (Δ_t) via online updates, genuinely altering behavior within a single episode. Evaluated on LoCoMo, LongMemEval-S, and CL-Bench, TMEM consistently outperforms summary-based and retrieval-based baselines.
StepPRM-RTL combines stepwise trajectory modeling, Process Reward Models, and retrieval-augmented fine-tuning to improve LLM-based RTL code generation. The framework uses MCTS to explore alternative reasoning paths and achieves >10% improvement in functional correctness on Verilog/VHDL benchmarks.
RUBAS is a rubric-based reinforcement learning framework for LLM agent alignment. It decomposes behavior into four dimensions (tool-use safety, argument safety, response safety, helpfulness) and generates structured rewards over complete trajectories. Experiments show improved safety and reduced hallucinations while maintaining utility.
EvalStop detects and stops RLHF jobs that overoptimize the reward model at the expense of real-world metrics. On 80% RLHF workloads (64 GPUs), the system achieves 98% precision and cuts wasted compute by 22% while improving JCT by 9% over SRTF-Est.
dMX is a differentiable mixed-precision quantization framework for learnable floating-point bit-width assignment across LLM layers. Tested on Llama, Qwen3, and SmolLM2 using the MXFP standard (Open Compute Project), it optimizes layer formats continuously then discretizes via annealing, outperforming KL-divergence heuristics on WikiText-2 and zero-shot reasoning benchmarks.
Systematic study of positional bias in multi-video summarization with MLLMs. Benchmark on ActivityNet and News videos (2-4 inputs). Evaluation of 9 models (open-source and proprietary) using three metrics: Coverage, Directional Positional Bias, Middle-Edge Gap. Finding: positional effects are domain- and model-dependent; increasing visual budget does not uniformly remove imbalance.