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Apple
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How to Guide Your Language Flow
Probe guidance, a novel method for guiding flow matching models, is introduced. It uses the frozen internal states of an existing diffusion model to construct a guidance signal, operating on a similar principle as autoguidance but removing the need for an additional forward pass at inference time. The method provides a reliable path ensuring the weak and strong models share similar dynamics. Probe guidance sets a new state-of-the-art performance on unconditional generation when benchmarked on continuous diffusion language models. Applied to a 1.7B diffusion language model, it consistently improves multiple choice question answering benchmarks.
Dynamically Scaled Activation Steering
Activation steering has emerged as a way to guide generative models toward desired behaviors, but uniform interventions can reduce performance when steering is unnecessary. DSAS, or Dynamically Scaled Activation Steering, addresses this by separating when to steer from how to steer and adapting intervention strength across layers and inputs. At generation time, it computes context-dependent scaling factors that modify existing steering transformations, and it can also be jointly optimized end-to-end with the steering function. Combined with existing methods, DSAS improves the reported Pareto front between toxicity mitigation and utility preservation, while adding minimal computational overhead; experiments also apply it to a text-to-image diffusion model for modulating specific concepts and show token-level interpretability through the strength and location of steering.
REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
REVERSAL-BENCH addresses the challenge of evaluating reset-free reinforcement learning when environments contain irreversible transitions. It controls reversibility with continuous parameter ρ ∈ [0, 1] and supplies a reset oracle, a ground-truth mechanism for verifying state recoverability across eight manipulation settings and five physics engines. Evaluations spanning standard actor-critic, safe reinforcement learning, and specialized reset-free methods find a sharp reversibility cliff: reset-free agents become absorbed into irrecoverable states as ρ increases, while episodic agents maintain steady learning. Geometrically identical reversible counterparts indicate the breakdown is caused by irreversibility rather than obstacle complexity, and the effect persists under learned policies in full physics simulations. The released suite includes a multi-simulator dataset with recoverability labels and a reset oracle; a safety shield predicts recoverability accurately, but active recovery mainly works when agents can steer away from traps.
DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models
DACA-GRPO addresses two weaknesses in reinforcement learning for diffusion language models: existing methods do not assign credit across denoising steps, and mean-field likelihood estimates are biased and high-variance. It is a lightweight, plug-and-play enhancement for GRPO-style trainers that combines Denoising Progress Scores with Stratified Masking Likelihood. The progress scores derive per-token importance weights from intermediate predictions without an additional forward pass, while stratified masking partitions token positions so each token is predicted with most of the sequence as context. Applied to three GRPO base methods, the approach improves results across seven benchmarks covering mathematical reasoning, code generation, constraint satisfaction, and constrained generation. Reported gains reach 5.6 percentage points on math reasoning, 7.4 on code generation, 36.3 on constraint satisfaction, and 5.9 on JSON schema adherence.
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
Discrete flow matching can parallelize text generation across positions, but reaching high quality may require hundreds or thousands of model evaluations. The paper argues that few-step distillation fails primarily because teacher trajectories are built from blind stochastic jumps: an early poor midpoint decision propagates, and the student is trained to imitate it. Its Trajectory-Shaped Discrete Flow Matching (TS-DFM) uses a lightweight energy compass during training to score candidate continuations at each midpoint and select the most coherent path, without adding inference cost. In experiments with 170M-parameter language models, an eight-step shaped student achieves 32% lower perplexity than a 1,024-step teacher and runs 128 times faster, with gains across source distributions and three evaluators; it also outperforms the compared discrete-generation baselines, including systems using more data or larger models.
How Value Induction Reshapes LLM Behaviour
Conversational LLMs are post-trained on language expressing traits including curiosity, open-mindedness, and empathy, alongside values such as helpfulness, harmlessness, and honesty. Because values are complex and inter-related, inducing one may alter others and may also affect safety, anthropomorphic language, addictiveness, or sycophancy. The authors fine-tune models with curated value subsets from existing preference datasets, then measure changes in other value expressions, safety, anthropomorphic language, and performance on various question-answering benchmarks. They report that induction can express related and sometimes contrastive values, while positive-value induction increases safety. Across the values studied, it also increases anthropomorphic language, making models more validating and sycophantic.
Shared Selective Persistent Memory for Agentic LLM Systems
Agentic LLM systems that generate code through multi-turn tool use lose configuration choices, domain constraints, schemas, and tool-use patterns when each session starts from zero. The proposed shared selective persistent memory retains task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces, and allows workspaces to be transferred across users with role-based access control. In a deployed collaborative workspace platform, agents create and maintain git-versioned dashboards, reports, and data-driven documents from CSV, SQL, REST API, and MCP sources; draft isolation supports risk-free changes and restoration without re-invoking the model. Results across three enterprise scenarios show 96% task completion, compared with 79% without memory and 71% with full history, while zero-token refresh cut recurring-update task time 14× and summary-driven generation reduced token cost 97× versus raw data injection.
Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs
Glyph addresses enterprise data lakes where tables accumulate faster than human stewards can document or classify them, leaving column descriptions and governance labels incomplete. It models column description generation and sensitivity-ontology tagging as cooperating LLM agents orchestrated as stateful graphs. The Descriptor uses active Retrieval-Augmented Generation to retrieve pipeline source code from an enterprise GitHub, while the Tagger runs description, line-of-business regex, and metadata strategies in parallel before combining ranked outputs with Reciprocal Rank Fusion. A fine-tuned six-layer MiniLM metadata encoder raised same-tag retrieval on an in-distribution held-out split from NDCG@10 0.55 to 0.92 and MAP@100 0.19 to 0.90, relative to the stock base encoder. Glyph also evaluates end-to-end multilabel tagging with a recall-weighted F2 objective across three groups and an ablation, emphasizing value-free, code-grounded operation, per-tag provenance, and graceful degradation for auditable production cataloging.
DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation
DiscoSign addresses a limitation in sign language processing: systems have traditionally translated at the sentence level, overlooking discourse phenomena important to sign language comprehension. It presents a modular Large Language Model (LLM)-based framework for text-to-sign language gloss translation that handles spatial coreference resolution, Question-Answer Clauses (QACs), and concept-gloss consistency, preserving entity locations and stable English-to-ASL sign mappings across discourse. Because traditional translation metrics do not measure discourse-level quality, the authors introduce metrics for each coherence dimension targeted by the framework. Experiments on sentence-level and discourse-level datasets report improved spatial consistency and entity tracking over sentence-only translation, while maintaining competitive single-sentence gloss quality; the work describes this as the first systematic framework with a corresponding evaluation methodology.
Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering
Evaluating video captions is difficult because reference-matching metrics can penalize valid descriptions that use different wording or focus on different visual content, while offering little fine-grained diagnosis. The paper reframes quality around information fidelity: captions should cover salient visual information and remain factually accurate. It introduces CapQuiz, a reference-free benchmark that tests caption utility through human-verified, fine-grained multiple-choice questions derived from videos, organized into 10 Descriptive and Inferential question types across 24 domains. It also proposes CapF1, which combines CapP for factuality with CapR for coverage; extensive experiments report significantly stronger correlation with human judgments than existing metrics and interpretable insight into model performance.
SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign
SimpleDesign addresses protein design as a joint modeling problem involving amino acid sequences and three-dimensional structures, whose interplay determines protein function. Unlike approaches that first train autoencoders and then model their latent representations, it uses a single-stage, end-to-end objective directly in data space. The objective combines discrete cross-entropy for sequences with a regression objective for structures, while Transformer-based multimodal backbones provide modality-specific processing and global self-attention across both modalities. Trained on more than 2M sequence-structure pairs, SimpleDesign achieves competitive performance on co-design and unconditional sequence and structure generation benchmarks. The work argues, based on these results, that multi-stage training is not necessary for performant co-design models.
REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs
REFACTOR-VLA addresses the weakness of monolithic vision-language-action (VLA) models, which generate raw commands or short action sequences and struggle with long-horizon tasks and interpretability. Its wake/sleep architecture uses a Behavioral-Equivalence Kernel (BEK) to cluster motor-program segments by outcomes from a learned latent world model, then produces typed lambda terms for a library-conditioned rectified-flow action decoder. Skills are admitted only when they satisfy both Minimum Description Length (MDL) and return-preservation gates, while training proceeds through world-model warmup, wake-phase policy optimization, and sleep-phase skill discovery. On the full LIBERO benchmark, expanding the world model from 188 million to 430 million parameters worsened results on all four suites, showing that scale alone was not beneficial. Adding supervised contrastive InfoNCE loss during warmup substantially improved sleep-phase clustering, with reported NMI scores of 0.462, 0.867, 0.915, and 0.754 for the Object, Spatial, Goal, and LIBERO-10 suites, respectively, under n = 3 multi-seeding.
LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs’ Probabilistic Beliefs
LLMs are deployed in medicine, science, and law, where observed evidence may not determine a single correct answer and systems must represent and update uncertain beliefs. The work treats LLMs as information processing rules and measures an information processing gap, defined as deviation from Bayes updates, to quantify internal consistency in probabilistic belief updates. Extensive experiments compare multiple ways of incorporating evidence: some yield nearly Bayesian updates, while others rely on learned heuristics. Although exact Bayesian updating is optimal information processing, the non-Bayesian heuristic updates often achieve better downstream task performance, suggesting that the LLMs’ probabilistic models of the world are misspecified. The measure also serves as a diagnostic for issues in LLM-powered inferential systems.
Agent Seer: Synthesizing Scenarios from Specification Understanding
Agent Seer addresses the problem of creating realistic, scalable test scenarios for AI agents that use external tools, where hand-built cases require domain expertise and static benchmarks cannot track changing APIs. It synthesizes scenarios from a single Model Context Protocol (MCP) specification containing function names, natural-language descriptions, and typed parameter schemas, without examples, live tool access, or domain-specific tuning. The pipeline enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded, multi-turn dialogues, then evaluates tool-calling correctness and conversational coherence across seven MCP specifications spanning domains and tool-suite sizes. It achieves strong quality across all domains and complete tool coverage on small and medium specifications; parameter schema complexity correlates most strongly with quality variation, while tool-suite size has a smaller orthogonal role. Among imperfect scenarios, argument value accuracy is the dominant failure mode, which coarse-grained name-match metrics do not reveal.
From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers
Designing reward signals for open-domain question answering remains challenging because high-quality answers must satisfy multiple distinct criteria that holistic scalar objectives fail to capture. To address this limitation, researchers developed a rubric-based reward framework that produces query-specific rubrics grounded in retrieved evidence and decomposed into specific quality dimensions. This structured reward setup provides fine-grained supervision during the post-training phase. Across the evaluation axes of composition, grounding, and instruction-following, the multi-dimensional grounded rubric method outperforms instruction-tuned baselines by 6.5% and flat rubric variants by 4%. Grounding rubrics in retrieved evidence bolsters factual support, while dimension decomposition enhances organization, coherence, and requirement adherence.
PROOF-Gen: From Optimized Data to Better Distillation
Standard distillation pipelines for tool-calling agents use generate-and-filter supervised fine-tuning on teacher trajectories, discarding failed trials and missing training signals on hard scenarios. On τ 2-bench, 57% of teacher trials fail, with two-thirds being near-misses undone by a single error. PROOF-Gen (Per-scenario Reflective Optimization to Overcome Failed Generation) addresses this loss by employing a reflector that reviews execution traces and evaluation feedback to craft corrective guidance for the teacher. Once passing trajectories are generated, this scaffold guidance is stripped so students train on clean demonstrations. Per-scenario prompt optimization recovers 93% of failed scenarios on τ 2-bench, boosting Qwen3-4B-Instruct-2507 Passˆ1 from 0.132 to 0.529 and lifting deployed on-device model performance across all locales.
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Recent advancements in large language models necessitate efficient deployment options within constrained inference budgets. While structured pruning offers better token efficiency than training target-sized models from scratch, previous work frequently ignores the pretraining of enlarged models. To resolve this gap, researchers developed an integrated enlarge-and-prune pipeline that unifies enlarged model training, pruning, and recovery under a single cosine annealing learning rate schedule. This system incorporates an iterative structured pruning technique to gradually eliminate parameters, redistributing model capacity across surviving neurons and avoiding the knowledge loss seen in naive approaches. Evaluated on compressing 2.8B parameter models to 1.3B across up to 2T pretraining tokens, the approach yields superior downstream model performance.
Luce: Relightable Gaussians for 3D Asset Generation
High-fidelity image-to-3D asset generation requires 3D representations that capture geometry alongside physically based rendering modalities for seamless pipeline integration and relighting. Luce addresses this challenge by unifying geometry and material properties into a voxelized multimodal Gaussian cloud with dedicated primitives for albedo, metallic-roughness, and surface normals. A variational autoencoder compresses these multimodal primitives into a unified material-aware latent space. A rectified-flow transformer subsequently generates this latent from a single input image using multi-layer features extracted from a pretrained image encoder. On the Toys4K dataset, Luce improves FID by 28% over the leading baseline while maintaining fine details such as text and logos.
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
Existing multimodal models struggle with structural fragmentation, often sacrificing visual fidelity via discrete tokenization or breaking structural symmetry by pairing causal text generation with diffusion-based denoising. STARFlow2 addresses these limitations by leveraging autoregressive normalizing flows, which share the causal mask, key-value cache mechanism, and left-to-right structure of large language models. Built upon the Pretzel architecture, the system vertically interleaves a frozen pretrained vision-language model stream with a TARFlow stream through residual skip connections under a single causal mask. A deep-shallow flow design combined with a unified FAE latent space allows both text and continuous visual outputs to enter the key-value cache directly without re-encoding. Benchmark experiments validate strong performance across multimodal understanding and continuous image generation tasks.
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
Visual chain-of-thought methods allow multimodal large language models to reason through intermediate image generation, but synthesizing explicit visual steps creates major inference overhead during proactive video reasoning. To eliminate runtime synthesis, Internalized Visual Thinking introduces a post-training framework that jointly optimizes textual prediction and next-embedding prediction across unlabeled videos. By predicting latent representations of future frames alongside target text answers during training, the model captures motion, object transitions, interactions, and latent intent. At inference time, the model produces answers directly without generating or re-encoding future frames. IVT outperforms text-only post-training across six evaluation settings while matching or exceeding Visual CoT performance and cutting end-to-end latency by more than 5x.
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