InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling
arXiv:2603.10049v1 Announce Type: new Abstract: Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using...
Improving Search Agent with One Line of Code
arXiv:2603.10069v1 Announce Type: new Abstract: Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools for a multi-turn information-seeking process autonomously. However, we identify a critical training instability that leads...
Digging Deeper: Learning Multi-Level Concept Hierarchies
arXiv:2603.10084v1 Announce Type: new Abstract: Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models...
Hardware Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs' high computational demands hinder edge deployment, as traditional ``hard'' sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a ``soft sparsity'' paradigm using a hardware...
CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR
arXiv:2603.10101v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training...
Lost in the Middle at Birth: An Exact Theory of Transformer Position Bias
arXiv:2603.10123v1 Announce Type: new Abstract: The ``Lost in the Middle'' phenomenon -- a U-shaped performance curve where LLMs retrieve well from the beginning and end of a context but fail in the middle -- is widely attributed to learned Softmax...
DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning
arXiv:2603.10180v1 Announce Type: new Abstract: The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs contain longitudinal observations of patients across hospital visits, where each visit is...
Actor-Accelerated Policy Dual Averaging for Reinforcement Learning in Continuous Action Spaces
arXiv:2603.10199v1 Announce Type: new Abstract: Policy Dual Averaging (PDA) offers a principled Policy Mirror Descent (PMD) framework that more naturally admits value function approximation than standard PMD, enabling the use of approximate advantage (or Q-) functions while retaining strong convergence...
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
arXiv:2603.10254v1 Announce Type: new Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high-quality synthetic...
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
arXiv:2603.10279v1 Announce Type: new Abstract: Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existing post-training methods are ill-suited for production-scale systems: RLHF methods reward hack due...
Regime-aware financial volatility forecasting via in-context learning
arXiv:2603.10299v1 Announce Type: new Abstract: This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market conditions. The proposed approach deploys pretrained LLMs to reason over historical volatility patterns and...
Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning
arXiv:2603.10377v1 Announce Type: new Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges...
Variance-Aware Adaptive Weighting for Diffusion Model Training
arXiv:2603.10391v1 Announce Type: new Abstract: Diffusion models have recently achieved remarkable success in generative modeling, yet their training dynamics across different noise levels remain highly imbalanced, which can lead to inefficient optimization and unstable learning behavior. In this work, we...
Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
arXiv:2603.10395v1 Announce Type: new Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has emerged due to its superior performance and flexible sampling. However,...
On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
arXiv:2603.10397v1 Announce Type: new Abstract: One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we...
Binance sues WSJ, panicked by gov’t probes into sanctioned crypto transfers
Binance’s lawsuit accusing WSJ of defamation unlikely to stall government probes.
Zendesk acquires agentic customer service startup Forethought
Forethought was years ahead of its time and the 2018 winner of TechCrunch Battlefield.
Replit snags $9B valuation 6 months after hitting $3B
Replit raised a new $400 million round and said it hopes to have $1B in ARR by year's end.
WordPress debuts a private workspace that runs in your browser via a new service, my.WordPress.net
WordPress’s new browser-based service lets users create private sites without hosting or signing up, turning the platform into a personal workspace for writing, research, and AI tools.
Canopii looks to succeed where past indoor farms have not
Canopii's robotic farms can autonomously grow 40,000 pounds of herbs and leafy greens a year while being the size of a basketball court.
What is a Tort?
What is a tort, and what is tort law for? On one leading scholarly account, torts are legal liability rules that seek to promote the welfare of society at large by disincentivizing socially suboptimal behavior and distributing the costs of...
When All Files Count the Same: The Problem of Undifferentiated Images in Child Pornography Sentencing
Our society generally agrees that possessing, producing, and distributing child sexual abuse material (CSAM) is morally reprehensible. This societal judgment is represented in sentencing...The postWhen All Files Count the Same: The Problem of Undifferentiated Images in Child Pornography Sentencingappeared first...
Reward Prediction with Factorized World States
arXiv:2603.09400v1 Announce Type: new Abstract: Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data, limiting...
Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search
arXiv:2603.08877v1 Announce Type: new Abstract: Agentic Retrieval-Augmented Generation (RAG) systems combine iterative search, planning prompts, and retrieval backends, but deployed settings impose explicit budgets on tool calls and completion tokens. We present a controlled measurement study of how search depth,...
Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts
arXiv:2603.09890v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of LLM-based multi-agents relies on ad hoc prompts and lacks a principled policy perspective. Different from...
Logos: An evolvable reasoning engine for rational molecular design
arXiv:2603.09268v1 Announce Type: new Abstract: The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either...
Telogenesis: Goal Is All U Need
arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive state. We propose a priority function that generates observation targets from three epistemic gaps: ignorance...
AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem
arXiv:2603.08938v1 Announce Type: new Abstract: The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Large Language Model (LLM)-based agents can autonomously operate local computing environments, orchestrate...
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
arXiv:2603.09022v1 Announce Type: new Abstract: Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are amplified by multi-agent coupling. This biases win rate estimates and makes rankings unreliable across...
A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
arXiv:2603.08954v1 Announce Type: new Abstract: The first 72 hours of a missing-person investigation are critical for successful recovery. Guardian is an end-to-end system designed to support missing-child investigation and early search planning. This paper presents the Guardian LLM Pipeline, a...