· ai
[Paper] Scaling Behavior of Discrete Diffusion Language Models
Modern LLM pre-training consumes vast amounts of compute and training data, making the scaling behavior, or scaling laws, of different models a key distinguishi...
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- · ai
[Paper] Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineerin...
- · ai
[Paper] Bayesian Symbolic Regression via Posterior Sampling
Symbolic regression is a powerful tool for discovering governing equations directly from data, but its sensitivity to noise hinders its broader application. Thi...
- · ai
[Paper] Learning Controllable and Diverse Player Behaviors in Multi-Agent Environments
This paper introduces a reinforcement learning framework that enables controllable and diverse player behaviors without relying on human gameplay data. Existing...
- · ai
[Paper] An Elementary Proof of the Near Optimality of LogSumExp Smoothing
We consider the design of smoothings of the (coordinate-wise) max function in mathbb{R}^d in the infinity norm. The LogSumExp function f(x)=ln(sum^d_iexp(x_i)) ...
- · software
[Paper] Zorya: Automated Concolic Execution of Single-Threaded Go Binaries
Go's adoption in critical infrastructure intensifies the need for systematic vulnerability detection, yet existing symbolic execution tools struggle with Go bin...
- · ai
[Paper] LabelFusion: Learning to Fuse LLMs and Transformer Classifiers for Robust Text Classification
LabelFusion is a fusion ensemble for text classification that learns to combine a traditional transformer-based classifier (e.g., RoBERTa) with one or more Larg...
- · ai
[Paper] The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models t...
- · ai
[Paper] Replace, Don't Expand: Mitigating Context Dilution in Multi-Hop RAG via Fixed-Budget Evidence Assembly
Retrieval-Augmented Generation (RAG) systems often fail on multi-hop queries when the initial retrieval misses a bridge fact. Prior corrective approaches, such ...
- · ai
[Paper] Script Gap: Evaluating LLM Triage on Indian Languages in Native vs Roman Scripts in a Real World Setting
Large Language Models (LLMs) are increasingly deployed in high-stakes clinical applications in India. In many such settings, speakers of Indian languages freque...
- · devops
[Paper] TriHaRd: Higher Resilience for TEE Trusted Time
Accurately measuring time passing is critical for many applications. However, in Trusted Execution Environments (TEEs) such as Intel SGX, the time source is out...
- · ai
[Paper] PACIFIC: a framework for generating benchmarks to check Precise Automatically Checked Instruction Following In Code
Large Language Model (LLM)-based code assistants have emerged as a powerful application of generative AI, demonstrating impressive capabilities in code generati...