Tootfinder

\textit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality
Yifan Zeng
https://arxiv.org/abs/2506.06622 https://

\textit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality
Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often hampered by issues of data hallucination and lack of access to real-time, verifiable financial information. This paper introduces QuantMCP, a novel framework designed to rigorously ground LLMs in financial reality. By leveraging the Model Context Protocol (MCP) for standardized and secure tool invocation, QuantMCP enables LLMs to accurately interfac…

This https://arxiv.org/abs/2505.19914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable diffic…

This https://arxiv.org/abs/2505.20302 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification
This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification methods to evaluate the quality and correctness of generated hardware descriptions. Additionally, we present a suite of specialized small-scale models optimized specifically for Verilog generation. Our work addresses the growing need for automated hardware design tools that can produce verifiably correct implementations …

This https://arxiv.org/abs/2506.05850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
We identify \textbf{Cross-lingual Collapse}, a systematic drift in which the chain-of-thought (CoT) of a multilingual language model reverts to its dominant pre-training language even when the prompt is expressed in a different language. Recent large language models (LLMs) with reinforcement learning with verifiable reward (RLVR) have achieved strong logical reasoning performances by exposing their intermediate reasoning traces, giving rise to large reasoning models (LRMs). However, the mechani…

Next Generation Authentication for Data Spaces: An Authentication Flow Based On Grant Negotiation And Authorization Protocol For Verifiable Presentations (GNAP4VP)
Rodrigo Men\'endez, Andres Munoz-Arcentales, Joaqu\'in Salvach\'ua, Carlos Aparicio, Irene Plaza, Gabriel Huecas
https://arxiv.org/abs/2505.24698

Next Generation Authentication for Data Spaces: An Authentication Flow Based On Grant Negotiation And Authorization Protocol For Verifiable Presentations (GNAP4VP)
Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization Protocol (GNAP) and integrates OpenID Connect for Verifiable Presentations (OIDC4VP) along with support for Linked Verifiable Presentations (LVP), providing a robust foundation for secure and privacy-preser…

Erdbeerzeit an der Amrumer Straße

CRScore : Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose
https://arxiv.org/abs/2506.00296

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main RL approaches, namely RL with Verifiable Feedback (RLVR) and RL with AI Feedback (RLAIF), offer trade-offs: RLVR provides reliable feedback for structured tasks like code generation, while RLAIF works for unstructured outputs but is subjective. We bridge this gap with CRScore++, an RL framework that leverages both LLM…

This https://arxiv.org/abs/2505.19641 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logica…

This https://arxiv.org/abs/2501.16007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
Large language models (LLMs) have proven to be very capable, but access to frontier models currently relies on inference providers. This introduces trust challenges: how can we be sure that the provider is using the model configuration they claim? We propose TOPLOC, a novel method for verifiable inference that addresses this problem. TOPLOC leverages a compact locality-sensitive hashing mechanism for intermediate activations, which can detect unauthorized modifications to models, prompts, or pr…

Verifiable Weighted Secret Sharing
Kareem Shehata, Han Fangqi, Sri AravindaKrishnan Thyagarajan
https://arxiv.org/abs/2505.24289 https://

Verifiable Weighted Secret Sharing
Traditionally, threshold secret sharing (TSS) schemes assume all parties have equal weight, yet emerging systems like blockchains reveal disparities in party trustworthiness, such as stake or reputation. Weighted Secret Sharing (WSS) addresses this by assigning varying weights to parties, ensuring security even if adversaries control parties with total weight at most a threshold $t$. Current WSS schemes assume honest dealers, resulting in security from only honest-but-curious behaviour but not …