Learning-Zone Energy (LZE) is an online data selection framework for RL post-training of LLMs. Tested on Qwen 1.5B-8B across GSM8K and MATH, it retains 40% of training data per step while matching full-data baselines, with OOD gains of +45.9% on AIME25 and 36% FLOP reduction.
CBT-Audio is a dataset of 1,802 patient turns from 96 public CBT recordings with expert-validated distress labels. Evaluation of 10 open-source audio language models shows audio improves distress estimation over text alone in 8/10 model families, with strongest gains when verbal content and vocal delivery diverge.
MEMOIR, a memory-guided tree-search framework, automatically synthesizes solvers for combinatorial optimization using LLMs. With a two-level memory hierarchy (branch-local and global), it achieves 96.7% solution validity across 7 problems (scheduling, routing, packing), outperforming baselines by 9.2 points and reducing run-to-run validity variance by over an order of magnitude.
GraphMind automates complex operational workflows by extracting action graphs from human resolution traces, then executes them via a multi-agent engine with LLM reasoning. An adaptive reinforcement mechanism (ATR) optimizes successful paths. Deployed across 4 cloud services, the system outperforms a Trace-RAG baseline with a 4.95/5 expert review score.
Dual-process memory architecture for scientific agents: decouples episodic window (10 messages) from semantic consolidation (3 tokens/message). Evaluation on 15,000 messages across 6 LLMs (OpenAI, Anthropic, Google): maintains 70-85% accuracy at 10,000 messages with 62% fewer tokens. Identifies trade-offs: Dual Process excels at numeric/temporal queries, RAG for historical retrieval.
Amazon Music deploys a robust neural sparse retrieval system for large-scale music search. The system handles misspellings, transpositions, and phonetic variations with 91.4% recall@10 on 6M documents, outperforming trigrams (57.7%). Inference-free architecture with granular subword tokenization (max 3 chars) and zero online latency.
KISS introduces a Knowledge Infrastructure (KI) enabling AI agents to execute complex Earth science simulations. On 3,000 trials, KI-equipped agents produced physically plausible simulations in 84% of cases vs. <40% without KI. An automated Knowledge Dissection Toolkit (KDT) generated 119 KIs across 14 Earth-science domains, showing operational expertise is structured and extractable rather than ad hoc.
LGBO (LLM-Guided Bayesian Optimization) embeds LLM semantic reasoning into every Bayesian Optimization iteration via a region-lifted preference mechanism. Tested on physics, chemistry, biology, and materials science benchmarks, LGBO reaches 90% of best observed value in 6 iterations for Fe-Cr battery electrolyte optimization, versus 10+ for standard BO.
TaskGround is a task-inference framework for household agents operating on complete scenes. It structures reasoning in three steps: grounding (extracting relevant context), inference (executable structure), execution (action sequences). Evaluated on FullHome (400 tasks), it improves success rates and makes Qwen3.5-9B competitive with GPT-4 while reducing token costs by 18x.
SD-Search introduces on-policy hindsight self-distillation for search-augmented reasoning agents. A single model acts as both student and teacher: the teacher, conditioned on past rollout outcomes, guides the student via token-level Jensen-Shannon divergence at query positions. No external teacher model or additional annotations needed.
KVDrive is a multi-tier KV cache management system for long-context LLM inference, orchestrating cache placement across GPU/DRAM/SSD, pipeline scheduling, and cross-tier coordination. The prototype achieves 1.74x higher throughput than state-of-the-art systems while preserving accuracy.
SkillGenBench is a benchmark for evaluating skill generation pipelines for LLM agents. It covers two regimes: task-conditioned generation and task-agnostic generation, with procedural sources grounded in repositories or documents. Experiments reveal substantial performance variation and distinct failure modes between software repositories and long-form documents.
A bank develops a conversational AI agent to triage fraud, scam, and dispute cases. The agent uses LLMs to ask targeted questions and route customers to appropriate specialist teams. Evaluation via synthetic digital twins simulating realistic dialogues. Result: +30.6% improvement in classification accuracy with compliance guardrails.
COAST (Contrastive Conceptor Activation Steering) improves Vision-Language-Action models by identifying and steering latent representations toward success-critical subspaces. Across three distinct architectures, COAST increases task success rates by +20% in simulation and +40% on real robots, without additional training.
LoopQ introduces a post-training quantization (PTQ) method for recursive language models (LoopLMs) that reuse Transformer blocks. It addresses three challenges: distribution shift across roles, state reuse between loops, and recursive error accumulation. Results: +68.8% downstream accuracy and -87.7% perplexity reduction in W4A4 vs strongest static PTQ baseline.
LARGER is a context retrieval framework for repository-level coding agents combining lexical search with structural graph exploration (imports, call chains, type hierarchies) without external databases. On LocBench, it improves file-level Acc@5 by +13.9 points (or +11.8 with fixed hyperparameters) and shows consistent gains on test generation and codebase QA benchmarks.
Critical study of state-of-the-art claims in AI/ML. Analysis of 10 public benchmarks reveals that over 50% of top-model comparisons fail to support implicit superiority properties (meaningful effect size, cross-task consistency, robustness). Aggregate gains often driven by outlier datasets. Proposes more honest claim language without additional experiments.
Researchers formalize activation steering (LLM control without retraining) as budget-constrained optimization. They introduce "concept granularity" to explain why some concepts are expensive to steer, and propose GRACE, a framework using activation geometry to diagnose difficulties and reduce evaluations needed by 39.8% on average.
GeoSym127K is a dataset of 127K geometric questions with exact symbolic ground truths, generated by an automated neuro-symbolic engine. Fine-tuning on Qwen3-VL-8B: +22.21% on MathVerse Vision-Only, 61.52% on WeMath. RLVR via GRPO further improves performance.
CLAP optimizes prompts in the latent space of Vision-Language-Action models to improve autonomous driving in rare safety-critical situations. Using contrastive learning and directional regularization, the method reduces planning error by 24% on challenging scenes (NAVSIM benchmark) with no regression on normal cases.
Empirical study on the impact of Google AI Overviews on Reddit. Using identification strategy based on moderation policy (SFW vs NSFW communities), authors find AI Overviews increase engagement in SFW communities by +12% (comments) and +12.3% (users), but only for experience-based content. Introduction of Google AI Mode eliminates these gains.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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.
RTPurbo transforms LLMs into sparse models in ~100 training steps. The approach exploits three observations: only certain heads require full attention, long-range retrieval uses a 16D subspace, and dynamic top-p selection outperforms fixed top-k. Results: 9.36× prefill speedup at 1M tokens, 2.01× decode speedup, accuracy preserved.
STT-Arena is a benchmark of 227 interactive tasks measuring LLM ability to replan under spatio-temporal dynamics. Claude-4.6-Opus achieves under 40% accuracy. Authors identify three recurring failure modes and propose STT-Agent-4B combining iterative trajectory refinement with online RL.
Systematic audit of two critical vulnerabilities in clinical AI: adversarial fragility and cross-lingual drift. On CheXNet (DenseNet121), accuracy collapses from 89.3% to 62.0% under imperceptible FGM perturbation (epsilon=0.021). Llama3.1:8b and NatLAS show major degradation on Nigerian Pidgin and Yoruba (80%→65%, 85%→55%). Standard defenses fail.
DynMuon extends Muon by replacing update M with U·Σ^p·V† using dynamic parameter p. Theory shows positive p accelerates signal contraction early by emphasizing high-curvature directions, while mildly negative p reallocates update strength to low-curvature directions late in training. DynMuon reduces steps to target loss by 10.6-26.5% versus Muon.
CAM-VFD detects video deepfakes by analyzing cross-modal contradictions (appearance, motion, depth) using cross-attention fusion. Achieves 95.31% on GenVidBench and 93.43% on GenVideo with robustness to compression and adversarial perturbations.
DashAttention introduces a differentiable hierarchical attention method using adaptive α-entmax transformation to select variable numbers of KV blocks. Unlike NSA and InfLLMv2, it maintains full differentiability and achieves 75% sparsity with accuracy comparable to full attention. GPU-aware Triton implementation outperforms FlashAttention-3.
Empirical study of RL post-training for diffusion-based code generation. Authors propose execution-free rewards (static checking) and AST-hint-conditioned sampling to overcome the "capability cliff". Static checking improves DiffuCoder from 53.9 to 67.1 on HumanEval and reduces rollout time by 9.4%.
TClone is a forkable personal workspace system for computer-use agents. It enables snapshotting, forking, and rollbacking live GUI environments with fast branching and isolation. Uses sibling containers, copy-on-write memory sharing, filesystem versioning, and asynchronous checkpointing. Reduces total task latency by 1.9x vs KVM and 1.5x vs CRIU.