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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state

1 Introduction

https://arxiv.org/html/2407.16741v3
edu Abstract Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways

Report GitHub Issue

https://arxiv.org/html/2510.03215v2
Report GitHub Issue Description: arXiv is now an independent nonprofit

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
Compared with the production DeepSeek-based LLM baseline, it improves screening precision from 92

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

1 Introduction

https://arxiv.org/html/2501.13956v1
com Abstract We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark

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https://arxiv.org/html/2608.03048v1
6 million tokens and find that it outperforms the recurrent-memory baseline by +6

Evaluating OpenAI’s Privacy Filter: Cross-Lingual, Cross-Domain PII

https://arxiv.org/html/2608.02616v1
com Abstract We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1

Report GitHub Issue

https://arxiv.org/html/2607.26245v1
We do not claim trading profitability—the central empirical finding is the opposite, and that negative result is published with the data that produced it

Computation and Language

https://arxiv.org/list/cs.CL/new
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
• We formulate sentence-level content planning as a combinatorial selection problem that combines relevance, factuality, and redundancy, and instantiate it with a DPP-inspired determinant-based quality–diversity objective

MinerU2.5: A Decoupled Vision-Language Model for Efficient High

https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface

1 Introduction

https://arxiv.org/html/2407.02518v2
com Abstract Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4
2 To what extent do multi-turn transmissions affect the evolution of properties

1 Introduction

https://arxiv.org/html/2506.01844v1
2 Related work Vision-language models (VLMs). Vision-language-action models (VLAs). 3 SmolVLA: small, efficient and capable Overview. 3.1 Model architecture Vision-language model (VLM). State, action, and feature projectors. Visual

Mathematics

https://arxiv.org/list/math/new
CO) Let $X=\{x_1,\dots,x_n\}\subset \mathbb{R}^2$ be a finite set of points of diameter at most $1$

[1910.03771v4] HuggingFace's Transformers: State-of-the-art Natural

https://arxiv.org/abs/1910.03771v4
03771v4] HuggingFace's Transformers: State-of-the-art Natural Language Processing Skip to main content arXiv is now an independent nonprofit

[1910.03771v5] HuggingFace's Transformers: State-of-the-art Natural

https://arxiv.org/abs/1910.03771v5
03771v5] HuggingFace's Transformers: State-of-the-art Natural Language Processing Skip to main content arXiv is now an independent nonprofit

[1910.03771] HuggingFace's Transformers: State-of-the-art Natural

https://arxiv.org/abs/1910.03771
03771] HuggingFace's Transformers: State-of-the-art Natural Language Processing Skip to main content arXiv is now an independent nonprofit

[1911.06462] Resource-Competitive Sybil Defenses

https://arxiv.org/abs/1911.06462
In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary

[1911.06462v1] Resource-Competitive Sybil Defenses

https://arxiv.org/abs/1911.06462v1
In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary

[1202.4083] Search for neutral Higgs bosons decaying to tau pairs in pp

https://arxiv.org/abs/1202.4083
4083] Search for neutral Higgs bosons decaying to tau pairs in pp collisions at sqrt(s) = 7 TeV Skip to main content Learn about arXiv becoming an independent nonprofit

[2006.03249] A Characterization of Semi-Synchrony for Asynchronous

https://arxiv.org/abs/2006.03249
In the semi-synchronous model (SSYNC), the robots share a common notion of time; at each time unit, a subset of the robots is activated, and each performs all three actions (sensing, computation, and movement) in that time unit

[2608.02414] Analyzing GPU Performance in Virtualized Environments: A~

https://arxiv.org/abs/2608.02414
unit (GPU) plays a crucial role in boosting application performance and enhancing computational tasks

[1606.08415] Gaussian Error Linear Units (GELUs)

https://arxiv.org/abs/1606.08415
Unit (GELU), a high-performing neural network activation function

[1702.01411] Maxwell and the normal distribution: A colored story of

https://arxiv.org/abs/1702.01411
If we take a charitable reading of his own proof of Proposition VI then it was Maxwell, and not Boltzmann, who gave the first proof of a tendency towards equilibrium, a sort of H-theorem

[2305.08291] Large Language Model Guided Tree-of-Thought

https://arxiv.org/abs/2305.08291
Our implementation of the ToT-based Sudoku solver is available on GitHub: \url{this https URL}