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30 result(s) for “okapi bm25 site hotchips pp site github com site to more site who unesco org site:arxiv.org” of 37 matched · 746 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/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
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/2407.16741v3
OpenHands has gained significant traction, with 32K GitHub stars and more than 2
https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound
https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03063v1
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Abstract
https://arxiv.org/list/cs.AI/new
We provide our implementation as an open-source framework to enable more diverse and creative AI deployments
https://arxiv.org/html/2607.22511v2
A common approach is to close the research loop with a large language model (LLM) reviewer
https://arxiv.org/html/2509.22186v2
In contrast to natural images, document images are characterized by significantly higher resolutions, denser content, and more complex structural layouts [20, 51, 52]
https://arxiv.org/html/2608.02609v1
By supporting composition in cuneiform in addition to reading, the system enables a more participatory mode of interaction with the script for non-specialist users
https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2608.02612v1
Recent datasets and benchmarks cover a range of optimization classes, from linear programming settings such as NL4Opt (ramamonjison2022nl4opt) to more recent resources for nonlinear optimization huang2025mamo\NAT@sep yang2024optibench\NAT@sep huang2025orlm\NAT@sep lu2025optmath, and support the evaluation on tasks
https://arxiv.org/html/2608.03446v1
cz
Abstract
Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the model
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2508.16279v1
We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace
https://arxiv.org/abs/2608.03883
Abstract:This paper describes our submissions to eRisk 2026 Task 3, ADHD Symptom Sentence Ranking
https://arxiv.org/abs/2104.08663
To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval
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/list/cs.CL/new
Accepted to C3NLP @ ACL 2026
Subjects: Computation and Language (cs
https://arxiv.org/html/2608.03486v1
Specifically, a general contrastive learning framework that makes use of this variation together with the assumed exponential-family structure is employed to recover the shocks
https://arxiv.org/html/2608.00785v1
com
Abstract
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs
https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2210.00131v4
This is distinct from the more well-studied form of spurious correlations: shortcut learning, in which the label is often specified given the features, yet the shortcut features are simply easier to learn than the intended features (Figure 1a) (Geirhos et al
https://arxiv.org/html/2508.19205v1
5B to 7B, the larger model exhibits significant gains in perceptual quality, delivering richer timbre, more natural intonation, and enhanced transfer capabilities, such as in cross-lingual applications
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
https://arxiv.org/html/2407.04503v4
For instance, we find that more open-ended instructions lead to stronger attraction effects compared to more constrained tasks
https://arxiv.org/html/2402.03300v3
com/deepseek-ai/DeepSeek-Math
Abstract
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature