Softmax Transformers implement in-context RL through equivalence to weighted softmax TD updates, with error decay under contraction and parameters as global minimizers of pretraining loss.
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Li, Adrien Bardes, Suzanne Petryk, Oscar Ma ˜nas, et al
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With specific linear Transformer parameters, CoT generation equals iterative TD updates, yielding geometric error decay with CoT length until a context-length statistical floor, and those parameters globally minimize the pretraining loss.
ArchSIBench is a new benchmark dataset and evaluation suite that measures vision-language models on architectural spatial intelligence across 17 subtasks, showing most models lag human baselines especially in transformation and configuration.
Zero-shot VLMs like GPT-5.2 achieve very low error rates on random forgeries in signature verification but perform poorly on skilled forgeries and are harmed by chain-of-thought reasoning.
Chain of Evidence introduces a retriever-agnostic visual attribution method for iRAG that reasons over document screenshots with VLMs to output precise bounding boxes, outperforming text baselines on Wiki-CoE and SlideVQA.
RouteHijack is a routing-aware jailbreak that identifies safety-critical experts via activation contrast and optimizes suffixes to suppress them, reaching 69.3% average attack success rate on seven MoE LLMs with strong transfer to variants and VLMs.
TF-SMOT composes pretrained vision-language models into a training-free pipeline that reaches state-of-the-art tracking and improved summary quality on the BenSMOT benchmark.
FineSightBench reveals VLMs perceive patterns down to 12px but show persistent failures in fine-scale reasoning such as numeracy and sequencing.
A multi-turn reflective dialogue between Teacher and Solver agents constructs richer context from support examples than standard in-context learning, improving video QA on the EgoCross benchmark.
OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
Proposes equivariant optimizer updates matched to layer symmetries for embeddings, SwiGLU MLPs, and MoE routers, with reported gains in validation loss and training stability on several language model architectures.
MegaScale-Omni delivers 1.27x-7.57x higher throughput for dynamic multimodal LLM training by decoupling encoder and LLM parallelism, using unified colocation, and applying adaptive workload balancing.
MoR lets clients train local reward models on private preferences and uses a learned Mixture-of-Rewards with GRPO on the server to align a shared base VLM without exchanging parameters, architectures, or raw data.
Proposes the Modality Translation Protocol with metrics ToS, CoS, FoS and SSC to quantify visual knowledge bottlenecks in VLMs, plus a Divergence Law hypothesis that scaling language models may increase the penalty.
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
SemanticOpt fine-tunes LLMs on structured Bayesian optimization trajectories augmented with natural-language context to jointly use numerical and semantic evidence for black-box optimization.
The paper benchmarks sycophancy in medical VLMs using hierarchical VQA templates and proposes VIPER to filter non-evidence social cues, reducing sycophancy while preserving interpretability.
Introduces FoodNExTDB dataset and EWR metric to benchmark VLMs for food recognition, showing closed-source models achieve over 90% EWR on single-product images but struggle with fine-grained distinctions.
TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
AC3S adds a self-supervised visual prompt modulator to ControlNet diffusion and a multi-agent VLM prompt composer to generate photorealistic images with accurate 2D/3D annotations while avoiding over-conditioning.
MVPruner is a two-stage adaptive token pruning technique for multi-view VLMs that achieves 87.3% FLOPs reduction and 4.97x prefilling speedup while retaining 98.5% accuracy on DriveLM.
Zero-shot VLMs reproduce aggregate human annotations on dwarf galaxy detection but exhibit high per-example variability and unreliable self-reported confidence.
HybridMoE with controlled hybridization and idiomatic property signals yields 5-6% gains in figurative language representation for multilingual vision-language models.
MGVQ introduces sensitivity-aware structured mixed-precision VQ and gradient-aware second-order error compensation using Kronecker and Block-LDL decompositions, reporting up to 4.9 point gains over prior methods at 2-bit on models like InternVL2-26B.
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