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VideoGPA uses a geometry foundation model to automatically derive dense preference signals and trains video diffusion models with DPO; the abstract says it improves temporal stability, geometric plausibility, and motion coherence with minimal preference pairs, but the snippet does not disclose dataset names, metric values, or model size.

The paper introduces AMD to detect and escape Forbidden Zones in few-step generation, raising SDXL HPSv2 from 30.64 to 31.25 and testing across image and video tasks including SDXL, Wan2.1, VBench, and GenEval.

The paper introduces CausalNeg with two modules: CoT-guided counterfactual perturbation for negative construction and query-view entropy maximization during training, targeting source-dependent shortcuts in generated hard negatives; the authors provide code on GitHub.

FLOWREADER models multimodal long-document evidence assembly as a min-cost flow problem and scores 58.40 on PaperTab and 72.93 on SlideVQA within VisDoMBench, outperforming G²-Reader by 1.30 and 0.62 on the two fragmented-evidence subsets.

DARP retrieves k-nearest expert demonstrations, actions, and relative distance vectors at inference time, then improves performance by 15–46% over standard behavior cloning across continuous control, robotic manipulation, and high-dimensional visual-feature settings.

CURE fine-tunes a multimodal instruction model with error-aware curriculum learning for radiology report generation without extra data, improving grounding by +0.35 IoU, increasing CXRFEScore by +0.192, and reducing hallucinations by 18.6% on public datasets.

BlendServe combines resource overlapping and prefix sharing with a resource-aware prefix tree, reorders latency-insensitive offline batch inference requests, and reports up to 1.44× higher throughput than vLLM and SGLang on synthetic multimodal workloads.

BrainSurgery modifies neural network checkpoints through declarative YAML plans; the arXiv abstract presents four examples and three case studies covering model upcycling and LoRA extraction.

ATLAS uses a lightweight verifier over intermediate hidden states to choose steering actions at inference time per example and step; the paper says it beats vanilla decoding and fixed steering on multiple math and coding benchmarks while reducing test-time tokens, but the abstract does not disclose exact scores.

TaskPGM learns continuous supervised fine-tuning task mixtures with a Markov random field, using unary utility and pairwise divergences from single-task fine-tuned models, and reports gains over uniform and size-proportional mixing on LLaMA-7B, Qwen2-7B, and BIG-Bench Hard.

VSD formulates draft training as variational inference over latent draft paths, then uses EM, Adaptive Rejection Weighting, and Confidence-Aware Regularization to increase expected acceptance length, with experiments reporting up to 9.6% speedup over EAGLE-3 and 7.9% over ViSpec across LLMs and MLLMs.

The paper introduces a behavioral cloning framework for scientific annotation with 9 synthetic tasks that model exploration, error correction, and strategic decisions; experiments show multi-task pretraining supports efficient fine-tuning to new tasks, while training from scratch fails entirely.

The paper defines a causal influence metric from log-probability differences across factual, counterfactual, and activation-patched VLM runs, then uses CD-T circuit discovery and concentration bounds to estimate the minimum counterfactual sample count m needed to detect instability in hallucinated outputs.

PriFT computes token weights from a frozen pretrained reference model rather than the online fine-tuned model, and experiments on mathematical reasoning, code generation, and medical question answering show stronger results than multiple SFT baselines plus better initialization for subsequent RL training.

The paper proposes a teacher-student spoofing detection framework without speaker labels, using a pretrained speaker recognition teacher, gradient reversal, and a Variational Information Bottleneck; across nine datasets, it reduces EER by 25.7% relative to the MHFA baseline.

MetaEvo proposes a two-stage framework: it first uses preference-based optimization to improve principle abstraction, then accumulates and reuses those principles in a modular agent architecture; the abstract says it outperforms strong baselines across reasoning benchmarks, but the post does not disclose exact scores.

The paper proposes R2, a training-free correction that projects each text embedding off the mean direction, and reports classification gains on MMTEB across 38 models, with 29 models showing t>2 and zero losses.

MPC-Flow formulates inverse problem solving with flow-based generative models as sequential control sub-problems, provides training-free inference-time guidance, and guides 32B FLUX.2 in a quantized setting on consumer hardware for image restoration tasks including in-painting, deblurring, and super-resolution.

The paper introduces SCMDM, a post-training adaptation for masked diffusion models that conditions each denoising step on the previous clean-state prediction, adds no extra denoiser evaluations during sampling, and reduces generative perplexity on OWT-trained models from 42.89 to 23.72.

arXiv:2603.14147v2 proposes domain-specific superintelligence as an alternative to monolithic scaling, using knowledge graphs, ontologies, and formal logic for synthetic curricula. The paper argues orchestration agents can route tasks across DSS back-ends, shifting capability toward smaller on-device experts; it does not disclose experiments, benchmarks, or energy measurements.

Safe-RULE proposes a defense framework for offline Safe RL that removes poisoned-data influence without retraining from scratch or accessing the original environment, and its unlearning process explicitly accounts for both task performance and safety constraints across benchmark Safe RL tasks.

The paper proposes a disjoint generation framework for tabular synthetic data: it partitions a dataset into disjoint subsets, fits separate generative model instances, and joins outputs without shared variables or identifiers. Case studies report higher empirical privacy measurements, improved feasibility for some model types, and mixed-model synthesis with competitive Accuracy and AUC while lowering empirical re-identification risk.

The paper introduces UPD, a population-free multi-agent reinforcement learning method that generates partners on the fly and selects them by learnability, reporting stronger performance than population-based and population-free baselines across Level-Based Foraging, Overcooked-AI, and the Overcooked Generalisation Challenge.

EinSort uses index ordering to discover low-rank structure in target tensors, and its weight and KV-cache compression experiments show better reconstruction quality than baselines.

The paper introduces the 2-Step Agent framework to model how a Bayesian decision maker updates beliefs from ML predictions and shows that one misaligned prior can make ML decision support produce worse downstream outcomes than no support, even with a well-specified model and a rational agent.

The paper evaluates 24 model variants from 270M to 8B parameters for merchant extraction, and Qwen 3.5 4B with JSON-only prompting reaches 96.60% F1, 0.35 points below the LLaMA 3.1-8B production baseline.

MOLOT models static call graphs as behavior sequences to detect malicious code in PyPI and npm packages, adds explanations mapped to source locations, and releases Open Malicious-Code Bench; the abstract does not disclose specific accuracy, latency, memory, or false-positive numbers.

The paper studies continuous diffusion language models through Embedded Language Flows and finds that frozen T5 token-embedding lookup recovers 93%–96% of native decoder decisions, while a single linear readout reaches 97.9% agreement on 32k samples.

LEAP learns unstructured pruning masks with a per-weight Bernoulli-via-Gumbel-sigmoid relaxation, and across five LLM families from 0.5B to 8B parameters at 50% and 60% sparsity, it improves six-task average zero-shot accuracy by 2.59 points over ADMM.

The paper formulates LLM post-training as a Discrepancy-Constrained Markov Decision Process, using Lagrangian relaxation to dynamically weight reward maximization against train-inference alignment, and reports improved RL stability and performance on Qwen-3-8B and Qwen-3-30B-A3B under black-box discrepancy.

The paper analyzes 15 calibration sources and shows that, on LLaMA-3.1-8B with SparseGPT at 60% sparsity, a uniform multi-source calibration mix reaches 58.8% total retention, 8.8 points above the best single source MetaMath and 18.8 points above the C4 default.

The paper proposes cross-tokenizer OPD using a precise token-mapping algorithm to transfer teacher probability distributions; the abstract says it is more compute-efficient than SFT baselines across multiple benchmarks, but the post does not disclose exact numbers.

The paper recasts manifold steering as Riemannian geodesic computation over activation space and trains an encoder on output distances from a small concept-token schema, avoiding per-prompt labels, topology priors, and per-task curve fitting.

The paper proposes DMLRank, a nonparametric framework that uses debiased machine learning to estimate GARS ranking scores from preference data, covering Bradley-Terry, PageRank/Rank centrality, ties, LLM-as-a-judge inputs, and budget-constrained preference collection policies.

The paper proposes truncating the SVD tail of fine-tuning update ΔW, reducing spurious-group gaps across three 0.5B–7B instruction-tuned models and four classification benchmarks while keeping accuracy loss under 2 percentage points.

The paper proposes AT-DPT, an adversarial training framework that jointly trains attackers to poison environment rewards and DPT to infer optimal actions from corrupted data, then evaluates it in bandit settings, adaptive attacker conditions, and MDPs.

Spherical KV compresses long-context KV cache with ADA and RDR, selecting keep, drop, and precision tiers per token and head under a fixed budget; the abstract does not disclose model scale, benchmark settings, or measured speedup numbers.

The paper introduces Contribution Weights, a projection-based metric using attention weight, value magnitude, and directional alignment to measure token influence, and reports better identification of semantically critical tokens than attention-only metrics across decoder-only models, tasks, and datasets.

The paper evaluates NVFP4 quantization on six edge-efficient models, reporting that block size B=16 needs 4.5078 bits per input at N=4096, while FP8 and FP16 weights add only modest accuracy gains over FP4 under the same NVFP4 activation path.

SMF improved MedMCQA by 2.5 percentage points on Qwen-2.5-0.5B-Instruct while keeping WikiText perplexity and TriviaQA accuracy within roughly 1 point of the base model; LoRA and full finetuning produced larger task gains but showed clearer drift on both forgetting probes.

The paper tests protected-label missingness on ACS/Folktables tasks and finds that positive-availability missingness usually does not move selected mitigation methods beyond the complete-label seed floor.

arXiv 2601.19082v2 tests LLM agents in a repeated Prisoner’s Dilemma, where higher payoffs make EGT predict more defection while LLMs become more cooperative; the authors also report the pattern in three smaller open-weight models.

GraphER builds a query-time graph from organizational proximity signals and reranks candidate documents for RAG, improving retrieval completeness across table retrieval, multi-hop retrieval, and long-document retrieval benchmarks without requiring additional graph infrastructure.

CHROMA augments RGB inputs with inter-channel correlation maps and trains a fixed CNN backbone; the paper reports that RGB and Lab color spaces give the clearest separation between real and generated images under its benchmark protocol.

SAEExplainer trains a feature explainer with activation scores as the reward signal and uses a two-round optimization loop to verify and correct explanations; the abstract says it beats established baselines on most metrics, with stronger results on causal triggering and discriminative activation.

The paper proposes a data-free early stopping framework for federated learning that monitors task-vector growth using only server-side parameters, and reports 12.3%, 8.9%, and 3.9% higher performance than validation-based stopping on skin lesion, blood cell, and colon pathology classification.

QueryWeaver converts natural-language queries into structured graphs and executes cross-tool dependencies with a deterministic planner and depth-first search; the arXiv snippet says it achieves high accuracy with smaller or locally hosted LLMs, but the post does not disclose accuracy numbers.

The paper tests Chronos for zero- and few-shot load forecasting on five real-world datasets, where it beats nine baseline models across 1–48 hour horizons and reduces RMSE by 7.34%–84.30% without dataset-specific fine-tuning.

STRIVE-D calibrates query rules with weakly labeled in-domain driving videos and fuses calibrated rule scores with vision-language and keyword retrieval signals, reporting up to 84% relative top-1 accuracy improvement over state-of-the-art methods across three driving benchmarks, including human-annotated DrivingDojo event data.

The paper introduces a dedicated confidence estimator that uses simulated annotator diversity and margin-based ranking to model LLM human-agreement versus disagreement cases, and reports higher fixed-sequence testing success rates across multiple datasets and judge models, while the abstract does not disclose dataset names or numeric gains.

The paper benchmarks four SSL feature extractors paired with four back-end classifiers across three training scenarios and six evaluation datasets; it reports domain bias in ASVspoof 5, where naive data scaling degrades performance, and finds that fine-tuning with 8 hours of target-language data improves spoofing detection robustness.

The paper analyzes LLM-generated text under a temperature-like control parameter and observes a phase transition; at the critical point, generated text shows power-law behavior similar to natural languages under a standard NLP metric.

Claw-R1 connects heterogeneous agent runtimes with RL training backends through a Gateway Server and Data Pool, capturing multi-turn interaction steps via a unified LLM API and storing step-level prompt IDs, response IDs, rewards, and metadata.

SG-OPD uses a binary verifier to filter teacher signals at rollout and token levels, and on competition-level mathematical reasoning benchmarks it beats standard OPD by 1.98 points per sample and 7.50 points per question.

OSMGraphCLIP learns global location embeddings from heterogeneous OpenStreetMap graphs and matches or exceeds satellite-based baselines on most evaluated geospatial tasks, including climate, ecology, socioeconomic indicators, public health, land cover, biodiversity, and wildfire forecasting.

ScaleSweep optimizes NVFP4 post-training quantization by sweeping feasible block-scale candidates and selecting the target-minimizing scale; experiments on Llama and Qwen show better initialization performance, and aggressive quantization of weights, activations, KV cache, and query states preserves over 93% of full-precision performance.

The paper introduces outcome-conformant synthesis and SpecBench; on a public dataset, off-the-shelf learned synthesizers miss the declared monthly aggregate by 74% to 86%, while the closed-form generator reaches 0 error.

The paper proposes a geometric framework for PTQ failure and QAT recovery: when the quantization grid is comparable to the low-loss basin width, local PTQ can select a high-loss deployed point outside the basin, while STE-based QAT evaluates gradients at quantized weights and updates latent full-precision weights.

arXiv 2606.07632 argues that ML resource accounting should use life cycle assessment, covering embodied hardware costs plus operational costs across training and inference rather than a single training run or one inference prediction.

Aco2 trains a quadrotor policy with a contextual observation encoder and contrastive objective, using simulation plus domain randomization, and the authors state it deploys to a physical lightweight-hook quadrotor without real-world fine-tuning.

CATPO filters RLVR training signal with tree informativeness scoring, critique-guided healing for all-failed branches, and an informativeness-weighted loss; on Qwen2.5-Math-1.5B trained with MATH, it reaches 37.5% macro accuracy across AIME24, MATH-500, OlympiadBench, and MinervaMath, 1.9% above TreeRPO and 4.8% above GRPO.

Flexible Context Parallelism dynamically reconfigures communication groups and context-parallel degrees during LLM and MLLM training, using a polynomial-time strategy search with millisecond overhead per batch and reporting up to 1.46x average throughput speedup over Megatron-LM and DeepSpeed, or 2.24x on extremely unbalanced batches.

The paper defines Lead-Lag Forecasting and releases two benchmarks: arXiv maps accesses to citations for 2.3M papers, and GitHub maps pushes and stars to forks for 3M repositories.

GNDPO transforms raw KL scores into batch-level relative advantages to stabilize on-policy distillation for MLLM reasoning, mitigating gradient explosions while preserving token-level guidance, and the authors released code at OPPO-Mente-Lab/GNDPO.

The paper proposes distributional reward models p(r|x,y) for RLHF reward hacking and derives a closed-form effective reward under KL-regularized objectives, showing that mean aggregation, worst-case optimization, and uncertainty-weighted optimization arise as limits or truncations of one expression.

The paper proposes a post-hoc per-sample trust score for extrapolative conditional generation, using only the training distribution and combining global realism with attribute-wise faithfulness; the authors report filtering, ranking, abstention, and downstream gains in biological imaging and controlled vision benchmarks, with code released on GitHub.

The arXiv paper proposes localized hardware ML architectures with lower bandwidth and higher per-node expressivity as alternatives to GPU-cluster neural networks, and the abstract says it evaluates candidate hardware paradigms by per-node expressivity, energy efficiency, and practical technology maturity.

The paper tests co-activation clustering on Pythia 1B, OLMo 1B, and OLMoE-1B-7B; two dense 1B models pass closure tests, while the MoE model’s ablation improves loss instead.

PBSD converts sparse final rewards into turn-level credit signals using a posterior-to-prior probability ratio, and the abstract says experiments cover in-domain and out-of-domain settings, but the post does not disclose benchmark counts or exact scores.

The paper proposes AGCLR, adding a three-gate persistent residual memory to CoCoNuT; GPT-2 experiments cover GSM8K, HotpotQA, and ProsQA, with vanilla CoCoNuT scoring 10.4% EM on HotpotQA versus an 11.0% EM CoT baseline.

The paper proposes Smoothed Full Fine-tuning, which linearly interpolates a randomly initialized auxiliary LTSM with pretrained weights to smooth the loss landscape and reports consistent downstream fine-tuning gains across eight LTSMs, including Timer, TimesFM, MOMENT, UniTS, MOIRAI, Chronos, TTMs, and Sundial.

DynaCF dynamically downweights high shortcut-sensitivity samples in the Bradley-Terry objective, using semantics-preserving counterfactual perturbations to track margin shifts and preference flips during optimization.

The paper proposes PVPO, a sample-efficient RL method that combines physical feasibility with voxel-space geometric rewards for LEGO assembly generation, and reports improvements across backbones and test-time scaling settings; the abstract does not disclose exact data fractions, benchmark scores, or model names.

arXiv 2606.08447 proposes an unsupervised sleep-like replay phase after sequential training on multiple tasks, partially restoring performance across previously learned tasks; the abstract does not disclose the number of tasks, model architecture, or recovery magnitude.

ConSteer-RL adds token-level log-probability confidence signals to GRPO reward shaping, penalizes overconfident errors, and reports average gains of 2.3%–4.0% over strong GRPO baselines across different model scales.

The paper proposes PoPE as a replacement for RoPE, reports lower perplexity on 124M to 774M language models, and shows stronger zero-shot length extrapolation than RoPE and YaRN, which requires extra fine-tuning and frequency interpolation.

AdaGRPO gates the GRPO objective with two rollout diagnostics, and on a large-scale e-commerce dataset it raises HR@10 from 11.01% to 12.18% while keeping hallucination below 0.22%.

STaR-Quant uses SGAT and TAC to address state-dependent activation disparity and temporal error accumulation in low-bit PTQ for diffusion LLMs, and experiments on representative DLLMs report up to 1.69x speedup and 3.14x memory savings versus FP16 deployment.

PIPE-Cypher turns a live property graph and optional seed queries into NL-to-Cypher benchmarks, uses local Qwen3.5-9B for generation and judging, exports 3,000 accepted FinBench/SNB examples, runs three audited ablation suites, and evaluates 11 local downstream models.

The paper proposes a unified framework for multi-domain data mixing losses, using Capacity Competition and Noise Reduction to predict effective training mixtures across unseen larger scales.

The paper proposes INNSteer, which learns an invertible neural network φ to map LLM activations into a latent space, applies latent translation at inference time, and maps back through φ⁻¹; the abstract says it improves control across multiple LLM families and safety benchmarks, but does not disclose model names, scores, or release details.

The researchers introduce a disturbance-aware reinforcement-learning framework for heterogeneous drone fleets, testing it on three species and four behavior models, where learned policies outperformed rule-based baselines while balancing observation quality against disturbance risk.

PairAlign models audio tokenization as conditional sequence generation, using cross-view self-alignment, EMA-teacher targets, prefix corruption, likelihood contrast, and length control; on 3-second speech retrieval tests, it preserves edit-distance search while reducing archive token count by 55%.

The paper proposes a voice conversion framework that uses KNN retrieval over WavLM representations to align non-parallel speech, builds synthetic training pairs, and adds a pretrained speaker-verification loss to preserve target-speaker identity.

The paper introduces a hierarchical rank-interval framework that builds task-level confidence intervals from pairwise comparisons and leaderboard-level prediction intervals with a conformal approach. Experiments cover simulated data, TabArena, and PromptEval (MMLU), and report statistically valid, informative intervals for uncertainty-aware model ranking.

The paper uses two attention metrics to separate local and global heads. The authors introduce three RL strategies that assign credit to preplan tokens, anchor tokens, and their temporal coupling across reasoning tasks.

LogNEO trains a GPT-Neo 1.3B log anomaly detector with PPO and a position-aware partial-credit reward, reaching F1 scores of 0.927, 0.913, and 0.984 on HDFS, BGL, and Thunderbird, with 45 ms end-to-end latency at 15,000 events/s in a Kafka, Redis, and TensorRT deployment.

The paper proposes an evidence-constrained AML triage framework that combines RAG evidence bundles, structured cited outputs, and counterfactual checks, reporting PR-AUC 0.75, Escalate F1 0.62, citation validity 0.98, evidence support 0.88, and counterfactual faithfulness 0.76 on public synthetic AML benchmarks and simulators.

arXiv 2606.08728 surveys AI for mathematical reasoning across four axes: informal text and diagram reasoning, formal reasoning in proof assistants, mathematical discovery, and inference or training-time techniques such as CoT, tool use, process reward models, and RLVR.

KITE integrates text, images, and Wikidata knowledge graphs into one Transformer, using Roberta, CLIP, and a GAT to encode three input types and output modality-specific confidence scores for fake news detection.

The paper proposes Deep Active Re-Labeling, which allocates part of the annotation budget to re-label already labeled data and uses two active noise sampling strategies to detect noise under different conditions.

The paper introduces the SDM activation function and SDM estimator for final-layer selective classification over pre-trained language models; the abstract says SDM is more robust than softmax-based calibration under covariate shifts and out-of-distribution inputs.

ReSkill embeds skill creation into GRPO training, using assertion-based diagnosis, within-group rollout comparisons, and Thompson Sampling with adaptive discounting to select skill versions as the policy evolves.

arXiv:2407.10247v3 proposes the CAIO Framework, using three AI properties and three organizational configurations to explain when companies create a Chief AI Officer role and when that dedicated role is effective.

SAILS fits generalized additive model surrogates to local effects of black-box models, detects pairwise feature interactions, and categorizes them into three forms: linear, product-separable, and non-product-separable.

The paper proposes a personalized review ranking and summarization framework and evaluates it on an Amazon Mobile Electronics review dataset plus a 70-participant user study, where its ranking method outperformed random ordering, star ratings, helpfulness votes, recency, and semantic-similarity baselines.

ReTabSyn fine-tunes a language-model-based tabular generator with reinforcement learning, prioritizing feature-correlation preservation and P(y|X) signals under limited data, and the abstract reports consistent gains over state-of-the-art baselines across benchmarks with small sample sizes, class imbalance, and distribution shift.

The paper introduces Comp-MCTS for counterfactual recourse generation under a fixed LLM-call budget; experiments on four real-world tabular datasets show higher yield of unique, oracle-validated counterfactuals than single-candidate LATS-style baselines.

The paper proposes DRPO, replacing DPPO’s hard mask with an advantage-weighted quadratic regularizer for policy shift; experiments cover model scales, architectures, and precision settings, but the abstract does not disclose specific improvement numbers.

The paper proposes a Maritime LLM post-training framework using RLVR to forecast 30-day vessel trajectories and destinations from 60-day AIS histories, and reports that 4B RLVR-trained variants deliver the best overall performance versus 8B and 14B models.

Lattice improves LSTM HR@10 by 31.7% on MovieLens with 30 paired seeds, using a validation-calibrated confidence threshold to activate behavioral-archetype scoring only when the in-support signal passes the gate.

The paper introduces ICR, a Fisher-based metric, to evaluate diffusion models’ representation and generation behavior. It reports peak invariance at intermediate noise levels and detects memorization in data-limited training from training features alone, without external evaluators or held-out test sets.

VESTA equips VLMs with a dynamically growing toolkit and is evaluated on DAWN across 3 tool settings, covering distribution fitting, time-series modeling, and astronomy tasks such as initial mass functions and gravitational-wave chirp signals.

LLMSynthor uses a pretrained LLM as a macro-aware simulator, iteratively generating record batches to reduce discrepancies between synthetic datasets and target aggregate statistics across mobility, e-commerce, and population domains.

SAW reweights multi-objective rewards with batch-level coefficient of variation, testing tool-calling and summarization under GRPO and GDPO; the arXiv snippet says it improves training efficiency and final performance but does not disclose specific scores.

The paper introduces CORAL, framing in-context reinforcement learning as a two-agent emergent communication problem. A pre-trained Information Agent acts as a fixed world-model contextualizer, while Causal Influence Loss measures each message’s effect on the next action; the abstract reports sample-efficiency gains and zero-shot adaptation, but does not disclose benchmark numbers.

The paper proposes a continuous hate-speech measurement system using 10 ordinal labels, 50,070 social media comments from YouTube, Twitter, and Reddit, and annotations from 11,143 US-based MTurk workers to train a RoBERTa-based model with faceted Rasch IRT adjustment.

CADFit recovers editable CAD construction sequences from meshes using IoU-driven optimization over structured CAD programs, supports extrusions, revolutions, fillets, and chamfers, and reports better volumetric IoU, Chamfer Distance, and Invalid Ratio than prior mesh-to-CAD methods on multiple benchmarks.

The paper proposes an LLM-based oversampling method for imbalanced classification and evaluates it on 10 tabular datasets against eight SOTA baselines, using minority-label-and-feature-conditioned generation, a permutation fine-tuning strategy, and interpolated samples to increase synthetic-sample diversity.

The paper introduces LARP, a learner-agnostic data prefiltering framework, proves worst-case loss upper bounds in 2 theoretical settings, and empirically measures its performance gap against learner-specific filtering across image and tabular tasks.

MedicalRec builds MedicalRec-Bench from 3,000 papers with over 5,000 medical image classification model records, evaluates four feature settings with 5, 9, 11, and 18 features, and reports a maximum HitRate@100 of 75.5%.

GeoGNN predicts the geographic origin of raw time series with a two-tower graph neural network, using geographic adjacency embeddings and temporal representations, and improves fine- and coarse-grained geolocalization accuracy by about 27% on average on countrywide electricity-consumption datasets.

The paper measures five trajectory-geometry metrics on GPT-2, TinyLlama, and Qwen2.5, reporting middle-to-late semantic convergence with peak CI of 0.41–0.58 and p<0.001.

CANS reduces average inference latency by up to 50% versus a non-cooperative baseline in prototype experiments on two edge devices, using shared device feedback and FedLinUCB-DW to learn DNN partitions under changing wireless links and heterogeneous device capabilities.

The paper proposes four design properties for graph foundation models in network dynamics; ts-net, trained only on synthetic multilayer networks, transfers zero-shot to real-world multilayer networks and beats classical heuristics and transductive baselines on three of four metrics.

The paper studies repeated pricing by multiple sellers using iterated least squares, comparing demand models that include competitor prices with oblivious models that ignore them; under sufficient exploration, prices converge to the competitive outcome, and the unique Nash equilibrium is an all-informed market.

AccioScene generates 3D indoor scenes from text prompts with a multi-stage pipeline: graph diffusion first produces a coherent scene graph, then layout prediction places objects, while human-object interaction priors and spatial constraints reduce interpenetration on 3D-FRONT experiments.

The paper proposes MI CAM, a post-hoc visual explanation method that weights feature maps by mutual information with the input image, combines them linearly with activation maps, and validates causal interpretations through counterfactual analysis.

arXiv:2606.09362 proposes a zero-shot autonomous-driving ReID pipeline that uses VLMs to generate structured textual attributes for traffic participants, and the abstract says its retrieval performance is comparable to a supervised CNN baseline while exposing viewpoint-driven attribute inconsistency and limited fine-grained discrimination.

The paper treats each candidate neuron as an arm, temporarily masks it on a sampled mini-batch, and measures loss changes; UCB1 gets the highest mean rank on tabular classification and regression tasks, while UCB1 and Thompson Sampling rank strongest on deep-learning tasks.

The paper models CBMs and SAEs as concept cones in activation space, then evaluates how SAE-learned cones approximate or contain CBM reference cones; the abstract does not disclose the model, dataset, metric values, or the reported sparsity and expansion-factor sweet spot.

KREPES interprets SSL representations for SimCLR, BYOL, and VICReg using Representer Landmarks, adds three transparency metrics, and uses a Nyström approximation framework to scale analytical inference to 1M-plus-sample benchmarks such as ImageNet-1K and Adult-1M.

VFEM converts multivariate time series into visual representations, uses a frozen large vision model plus cross-modal attention to fuse temporal features, and trains only 7.45% of total parameters while reporting competitive results on multiple benchmarks.

The paper separates attention sinks into adaptive nop and broadcast mechanisms, then diagnoses pretrained vision transformers using value norms and low-rank outputs, finding sinks shift from CLS tokens in early layers to patch tokens in deeper layers and concentrate in specialized heads.

arXiv:2409.15723v3 surveys FedLLM and covers federated fine-tuning, federated prompt learning, federated pre-training, and federated agents; the snippet does not disclose the number of reviewed methods.

Echo-Memory fixes the action-to-video interface and changes only memory storage and readout, comparing raw context, compression-based memory, spatial summaries, and state-space recurrence under one shared video diffusion backbone.

The paper releases RokomariBG, a Bangla book knowledge graph with 127,302 books, 63,723 users, 16,601 authors, 1,515 categories, 2,757 publishers, and 209,602 reviews, and benchmarks representative models on top-N and sequential recommendation tasks.

arXiv:2509.09151v2 frames video-understanding progress around dataset structure, linking invariances, inductive biases, and architectures across two-stream networks, 3D CNNs, temporal models, transformers, graph methods, and multimodal foundation models.

The paper introduces Anomaly Preference Optimization for anomaly image generation, using real anomalies as positive references and deriving signals from denoising trajectory deviations; the RSS snippet does not disclose datasets, metric values, or baseline names.

TRIAGE trains a single LLM to generate outcome-specific dialectical rationales for irregularly sampled medical time series, improving average AUPRC by 3.3% and reducing calibration error by 81% across three ISMTS benchmarks versus competitive baselines.

The paper proposes ADAP, a shellwise adaptive generate-rank-verify algorithm, and proves under a monotonicity assumption that its expected cost stays within a constant factor of the distribution-aware optimum.