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Newest papers, page 186
Reasoning Reranking with Semantic IDs as the Sole Item Representation
Reasoning rerankers write a rationale before ranking a candidate slate, and existing systems read item metadata to do so, even though generative recommenders increasingly represent items as compact…Replay Pruning and Few-Bit Integer Inference for Hardware-Efficient Speed-Sign Recognition
Speed-limit sign recognition on an automotive camera system-on-chip (SoC) must fit a detector and a classifier (the reader) into the memory, arithmetic, and energy budgets of the device.Causal Edit Serialization
Standard autoregressive language models generate text sequentially, but revision requires referring back to tokens in the input context or those already generated.Partial-Revelation Panel Forecasting: Predictive Information Gain, Allocation, and Coarsening-Consistent Operators
We study partial-revelation panel forecasting: given $k$ of $n$ annotator votes on the previous dialogue turn, predict the full count distribution of the next turn's panel.Benchmarking and Enhancing Agentic Causal Discovery
LLM agents can now run causal discovery algorithms on a dataset and report what comes out.ToPA: Block-wise Toeplitz Adaptation for Expressive and Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prevalent strategy for adapting large pre-trained models.Learning to Be Guided: Shaping Latent Geometry for Classifier-Free Guidance
Classifier-free guidance (CFG) is a standard component of conditional diffusion models, and guided generation performance has become the \textit{de facto} measure of state-of-the-art models.Batched Contextual Reinforcement II: Does RLVR Need Long Rollout Windows?
Long rollout windows make reinforcement learning with verifiable rewards (RLVR) expensive, yet simply shortening them can remove the correctness signal: when every single-problem rollout truncates…What Accuracy Hides In Learned Transformer Programs
Transformer Programs (TP) (Friedman et al., 2023) constrain a Transformer's architecture so that its learned computation can be extracted as readable Python code.Competence-Based Evaluation of Logical Reasoning in Large Language Models
Recent advances in large language models (LLMs) have brought impressive performance across diverse tasks; however, shortcomings remain in logical reasoning, including inconsistencies under…When to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents
Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses.A Feature Selection Layer Approach for Multi-Class Classification on Neural Networks
In the age of data-driven decision-making, deep learning models have become powerful tools for uncovering patterns and making predictions that influence many aspects of society.FederatedBytes: Tokenizer-Free, Byte-Level Small Language Models for Efficient Multimodal Federated Learning
Federated Learning (FL) is a powerful machine learning framework that allows building a global model from the contributions of multiple, distributed clients, while ensuring the privacy of their…Where's the Indirect Evidence? Investigating Grammatical Generalization with Influence Functions
Language models generalize beyond their training corpus to learn linguistic phenomena (Patil et al., 2024; Misra and Mahowald, 2024), showing that there exists indirect evidence driving learning.Scalable Levenberg-Marquardt Training Framework for Deep Learning
The Levenberg-Marquardt (LMA) algorithm is a widely used nonlinear least-squares solver known for its rapid convergence.Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not…Many Biases, One Circuit: Post-Training Tunes a Pretrained Cue-Routing Mechanism
Language models often abandon a correct answer when the prompt contains a cue, a hint toward a wrong option such as "I think the answer is B." Sycophancy and related biases show this failure.Aligning Language Models Towards Counterfactual Invariance
Small variations in prompt characteristics can meaningfully alter the semantics, tone, and contextual assumptions of large language model outputs.Large Diffusion Models Need More Than EMA
Large-scale diffusion models are commonly trained with a constant learning rate (LR) and exponential moving average (EMA), despite the strong performance of LR decay and Schedule-Free optimization…On the tradeoff between robustness and geometric accuracy in adversarial training
For non-parametric binary classification in the presence of an adversary, we prove an optimal quantitative estimate for adversarial classifiers controlling geometric accuracy, as measured by the…