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This paper investigates how Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning by focusing on the *direction* of policy updates rather than their magnitude. The authors introduce $\Delta \log p$, the signed log-probability difference between base and RLVR models, and argue it better captures reasoning-critical tokens than magnitude-based metrics like entropy or KL divergence. They validate this through token-replacement interventions and propose two practical applications: a test-time extrapolation method that amplifies the learned direction without additional training, and a training-time reweighting scheme that focuses learning on low-probability tokens.
Large language models often lack coverage in specialized, data-scarce domains where web text is limited. This paper proposes SPA (Scaling Prompt-engineered Augmentation), a baseline that generates large-scale synthetic corpora using just seven carefully designed prompt templates grounded in cognitive learning strategies (Concept Learning, Critical Thinking, and Generative Learning). The core finding is that this simple approach consistently outperforms complex RL-based methods like SEAL and multi-stage pipelines like EntiGraph across Wikipedia QA, long-document comprehension, and multi-hop reasoning benchmarks, suggesting that careful prompt design combined with straightforward scaling is surprisingly effective for knowledge injection.
Personalized image generation with diffusion models relies on Low-Rank Adaptation (LoRA) to fine-tune models efficiently, but current practice uses a fixed rank across all layers regardless of subject complexity. This paper proposes LoRA2, which learns adaptive ranks per LoRA component via a variational framework that imposes an importance ordering over rank indices using a discretized exponential distribution. The method achieves better subject fidelity and prompt alignment while using significantly less memory than high-rank baselines, addressing the combinatorial explosion of searching $S K^L$ architectural configurations.
Multi-Objective Reinforcement Learning (MORL) agents must balance competing objectives like speed versus energy consumption, yet existing Explainable RL methods fail to clarify how specific behavioral choices drive Pareto trade-offs. This paper proposes TREX, a post-hoc trajectory attribution framework that clusters agent behaviors into semantically meaningful segments and quantifies each cluster's influence on objective trade-offs by training complementary policies that exclude specific trajectory groups. The work addresses a genuine gap in explainability by moving beyond policy selection to reveal which behavioral patterns (such as "long leaps" versus "short strides") justify the agent's learned trade-off logic.
The paper addresses sample-efficient selection among multiple pretrained generative models, formulated as a diversity-aware multi-armed bandit problem where the optimal solution may be a mixture rather than a single model. The authors challenge the necessity of explicit UCB exploration bonuses, proposing that Mixture-Greedy—which directly optimizes empirical diversity objectives without optimism bonuses—can achieve sublinear regret through implicit exploration induced by the objective geometry. This matters because sampling from suboptimal generative models is computationally expensive, and their results suggest that structural properties of diversity metrics (FID, Vendi, RKE) naturally enforce sufficient exploration without costly confidence bound computations.
Constraint-based causal discovery algorithms like PC require exponentially many conditional independence (CI) tests in the worst case---specifically $p^{\mathcal{O}(d)}$ where $d$ is the maximum degree. This paper establishes that the fundamental complexity parameter is actually $s$, the maximum undirected clique size in the essential graph, which can be much smaller than $d$ (e.g., $s=2$ vs $d=p-2$ in Figure 1). The authors propose Greedy Ancestral Search (GAS), which achieves $p^{\mathcal{O}(s)}$ CI tests, and prove a matching lower bound of $2^{\Omega(s)}$, establishing exponent-optimality up to a logarithmic factor.
Standard Transformers apply fixed-depth computation regardless of problem difficulty, limiting their ability to solve tasks requiring variable-depth reasoning like multi-hop traversal or nested logic. This paper proposes a depth-recurrent Transformer that iteratively applies a shared-weight block in latent space—enabling 'vertical Chain-of-Thought' where models trade recurrence steps for deeper reasoning without consuming context window. The work demonstrates strong compositional generalization on three synthetic tasks and offers a mechanistic alternative to horizontal token-generation paradigms.
Long-context LLM inference hits a memory wall: each decode step requires scanning the entire KV cache, incurring $O(n)$ memory bandwidth that cannot be solved by faster arithmetic. PRISM proposes a thin-film lithium niobate photonic accelerator that performs the block-selection similarity search in $O(1)$ optical latency using a broadcast-and-weight architecture, eliminating the $O(n)$ scan entirely. The work claims $16\times$–$32\times$ traffic reduction at 64K–128K tokens and a four-order-of-magnitude energy advantage over GPU baselines by matching photonic hardware capabilities—passive query broadcast, quasi-static microring weights, and low-precision rank output—to the selection task.
This paper tackles the domain generalization problem in image deraining, where models trained on synthetic data fail catastrophically on out-of-distribution (OOD) real-world scenarios. The authors propose a three-stage pipeline—Superpixel Generation, Resolution-adaptive Fusion, and Pseudo-label Re-Synthesis—that adapts source-domain models to target domains using only unpaired rain-free images, eliminating the need for costly paired rainy data collection.
This paper studies generalization error bounds for Transformer models using offset Rademacher complexity. The core idea is to derive sharp excess risk bounds that achieve optimal $O(1/n)$ convergence rates---improving upon the standard $O(1/\sqrt{n})$---for single-head, multi-head, and multi-layer architectures, with explicit dependence on matrix ranks and parameter norms. The authors further extend these results to unbounded sub-Gaussian and heavy-tailed input distributions, broadening the applicability beyond standard boundedness assumptions.
Rule-State Inference (RSI) addresses compliance monitoring in domains like taxation where authoritative rules are known a priori but observations are partial, noisy, or strategically distorted. The paper proposes a Bayesian framework that inverts the standard ML paradigm: instead of learning rules from data, RSI encodes regulatory rules as structured priors and infers latent rule states (activation, compliance rate, parametric drift) via posterior inference. This enables zero-shot compliance assessment without labeled training data—a critical capability for low-resource environments where non-compliance labels are scarce or legally sensitive.
Concrete mix design requires balancing competing objectives of mechanical strength and sustainability. BOxCrete introduces a Gaussian Process regression framework trained on 533 strength measurements from 123 unique mixtures to predict compressive strength evolution over curing time and optimize mixes for embodied carbon using multi-objective Bayesian Optimization. The work addresses a critical gap in the literature by providing an open-source alternative to proprietary industrial datasets and models.
This paper addresses computational barriers for Brazilian Portuguese question answering by systematically evaluating Parameter-Efficient Fine-Tuning (PEFT) methods on BERTimbau models using the SQuAD-BR dataset. The authors test LoRA, DoRA, QLoRA, and QDoRA across Base (110M) and Large (335M) variants, demonstrating that LoRA achieves 95.8% of full fine-tuning performance while reducing training time by 73.5%. A key finding is that PEFT methods require substantially higher learning rates ($2\times 10^{-4}$) than standard BERT fine-tuning to achieve optimal results, with quantization resilience favoring larger models.
CataractSAM-2 adapts Meta's Segment Anything Model 2 (SAM-2) for real-time semantic segmentation in cataract surgery videos. The core idea is to fine-tune only the prompt encoder and mask decoder while freezing the image encoder, enabling precise segmentation of anatomical structures and surgical instruments under challenging conditions like glare and occlusion. The paper also introduces an interactive annotation framework that propagates sparse user prompts across video frames to accelerate ground-truth generation.
The paper tackles the inefficiency of homogeneous compute allocation in multi-task supervised fine-tuning (SFT), where fast-learning tasks overfit while slow ones remain under-trained. The authors propose mSFT, an iterative algorithm that dynamically excludes overfitting sub-datasets and reverts to optimal checkpoints. Their approach consistently outperforms baselines across 6 models and 10 benchmarks, sometimes reducing compute while improving accuracy.
This paper addresses the fundamental problem that correlational sentiment analysis cannot distinguish genuine economic associations from spurious statistical artifacts in financial markets. The core contribution is a refutation-validated framework for aspect-based sentiment analysis that combines net-ratio sentiment scoring with four robustness tests—placebo, random common cause, subset stability, and bootstrap validation—to filter false discoveries in high-dimensional sentiment-return analysis. This matters because investment strategies built on spurious correlations can lead to systematic losses, and regulators increasingly demand explainable AI systems with auditable validation.
This paper introduces "silent commitment failure" — a phenomenon where instruction-tuned language models produce confident, incorrect outputs with no detectable pre-commitment warning signal — and proposes "governability" as a measurable property for AI agent safety. The core claim is that 2 of 3 instruction-following models evaluated exhibit zero-warning failure modes, with profound implications for autonomous agent deployment. The work distinguishes itself from hallucination studies by focusing on detectability before commitment rather than correctness of output, and presents empirical evidence that conflict-detection signals (the "authority band") are geometric properties fixed at pretraining rather than injectable through fine-tuning.
This paper proposes Riemannian Foundation Model (RFM), a vision for unifying graph learning through Riemannian geometry rather than GNN message-passing or LLM serialization. The authors argue that graphs are discrete analogs of manifolds, and that concepts like vector bundles, curvature, and parallel transport provide the proper toolkit for universal graph modeling—enabling both structural inference and generation in a way that current Euclidean GNNs and tokenized LLMs cannot achieve.
Preference alignment typically requires expensive weight-updating training like RLHF or DPO, which lacks mechanistic interpretability. This paper proposes DSPA, an inference-time method that dynamically steers sparse autoencoder (SAE) features based on prompt content without modifying base-model weights. By computing a sparse conditional-difference map $\mathbf{A}$ from preference triples that links prompt features to generation-control features, DSPA edits only token-active latents during decoding. The method achieves competitive open-ended generation quality with up to $4.47\times$ fewer alignment-stage FLOPs than training-based alternatives, while offering direct auditability of which features are modified and revealing that preference directions are dominated by discourse and stylistic signals.
Machine learning models on mobile devices spend 61-86% of execution time extracting features from user behavior logs rather than running inference. This paper introduces AutoFeature, a graph-based engine that eliminates redundant operations across features and consecutive executions using directed acyclic graph optimization and intelligent caching. Tested across five industrial services including TikTok and e-commerce platforms, it achieves 1.33×-4.53× end-to-end latency reduction without accuracy loss.