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21 result(s) for “okapi bm25 site hotchips pp site github com site via di unit it site draft css site:arxiv.org” · 944 ms
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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
https://arxiv.org/html/2608.02613v1
Instead, it falls on local, personal agents to effectively and safely maintain/utilize such dense person-person (PP) and person-agent (PA) interaction histories
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
https://arxiv.org/list/cs.CL/new
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
https://arxiv.org/html/2608.03703v1
An LETF must trade in the direction of the day’s move at a close that also measures it, so its demand rises in price, and arbitrageurs buying ahead of it enlarge the order by trading at the print, then unload into what they manufactured
https://arxiv.org/list/math/new
It is natural to expect that if many pairs $(x_i,x_j)$ lie at distance close to $1$ from each other, then some clustering phenomenon must occur, implying that a significant number of these pairs are also very close to each other
https://arxiv.org/html/2608.03048v1
6 million tokens and find that it outperforms the recurrent-memory baseline by +6
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
https://arxiv.org/html/2608.03138v1
since it requires coordinating background, gap identification, method and contribution within a coherent narrative
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
https://arxiv.org/html/2608.03154v1
We evaluated it on three BioRE benchmarks (SemRepGS, DDI, and ChemProt) using both proprietary and open-weight LLMs
https://arxiv.org/html/2608.02609v1
Code and pretrained model: https://github
https://arxiv.org/html/2607.22511v2
Because a machine-checked proof establishes only that a formal statement follows from its assumptions—not that the statement faithfully captures the intended scientific claim—the pipeline augments kernel verification with a statement audit that compares each formal theorem against the informal claim it is intended
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
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
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
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
https://arxiv.org/html/2608.02625v1
This configuration is not primarily a speculative-speed system; rather, it isolates whether bidirectional correction is a useful decoding primitive when the draft and refiner are maximally matched
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.IR/new
To handle it, KGD assigns them to separate parameter sets: a refreshable encoder owns behavioral knowledge, while a task learner reads contextualized encoder states through read-only cross-attention and writes task-specific geometry through Anchored Calibration Residual (ACR) orthogonal to the pretrained embedding
https://arxiv.org/html/2608.02817v1
The authors are grateful for the invaluable feedback they received on an earlier draft from Jón Steinsson
https://arxiv.org/html/2607.26327v3
I thank an anonymous reader of an earlier draft for comments that prompted the institutional extension in Section 4, and further rounds of critical comments for prompting the power analysis of Appendix C, the coverage-boundary discussion in Section 4