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pgvector+17% downloads · 9.1M/wk+17.1%
MarkItDown+16% downloads · 3.9M/wk+16.3%
Chroma+15% downloads · 2.2M/wk+15.4%
GraphRAG+30% downloads · 16.2k/wk+29.7%

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Open problems in RAG

Effective use of retrieved evidence in generation2 papers raise it · 2 address it
Models often ignore or misuse available evidence, leading to outdated or unfaithful answers despite retrieval.
Long-context reasoning difficulty beyond length1 paper raises it · 0 address it
Performance degrades when evidence is scattered, sparse, or requires multi-step reasoning, not just long context.
- Beyond Context Length: Characterising the Difficulty of Long-Context Reasoningraises
KV cache compression for long contexts1 paper raises it · 1 addresses it
Large KV caches increase memory and compute costs, but compression must preserve information for unseen queries.
- Latent Compaction with Test-Time Trainingraises · addresses
Privacy risks in RAG datastores1 paper raises it · 1 addresses it
Retrieval can leak sensitive information from external knowledge sources, requiring privacy-preserving methods.
- Private Retrieval Augmented Generation via Random Projectionraises · addresses
Visual evidence grounding in long documents1 paper raises it · 1 addresses it
Localizing and interpreting visual evidence (charts, tables) in long documents remains a bottleneck for QA systems.
- VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documentsraises · addresses

Named by an AI from these papers' abstracts.

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Active contributors · 90 days

Dixi YaoPrivate Retrieval Augmented Generation via Random Projection1
Tahseen RabbaniPrivate Retrieval Augmented Generation via Random Projection1
Tian LiPrivate Retrieval Augmented Generation via Random Projection1
Meenakshi SyamkumarMulti-sourced AI Tutor for Non-Native English Speaker Students1
Rogers Jeffrey Leo JohnMulti-sourced AI Tutor for Non-Native English Speaker Students1
Weibing WangMulti-sourced AI Tutor for Non-Native English Speaker Students1

Active contributors

Activity · 12 weeks

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★ Spotlight this month

arXiv cs.CLCodeOct 14▲ 8

Latent Compaction with Test-Time Training

Yingfa Chen, Xingyu Shen, Chaojun Xiao, Xu Han

Long contexts enlarge key--value (KV) caches, increasing memory use and attention costs during generation. Compressing these caches must preserve information for unseen questions. We introduce **Latent Compaction**, which learns a compact document-specific cache through test-time training. We initialize cache entries using reconstruction importance, teacher attention, and key-space diversity. Training first fits attention outputs and log-normalizers to those of the full cache, with reconstruction-selected entries held fixed. It then jointly optimizes the entire cache through end-to-end distillation from the full-cache model. Model weights remain frozen throughout, and training uses only generic prompts about the document, without access to downstream questions or answers. Experiments on RULER, LongBench, and Phonebook Lookup demonstrate substantial gains over budget-matched compression baselines. On a RULER, for example, our method achieves 96.7% accuracy at 32$\times$ compression, compared with 40.0% for KVZip+. The method also transfers across model scales and families, with retrieval gains on both Qwen3 and MiniCPM5. These results show that treating the KV cache as a neural…

PDF arXiv page Details & code

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arXiv cs.AIOct 15
Private Retrieval Augmented Generation via Random Projection
Dixi Yao, Tahseen Rabbani, Tian Li
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it can also introduce privacy risks by leaking sensitive information from the retrieval datastore. We propose a simple method to preserve datastore privacy in RAG systems via random projection. By applying the same projection to both datastore embeddings and query embeddings, our method provably preserves semantic similarity between queries and retrieved items while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets, we show that this lightweight approach achieves superior retrieval and generation performance compared to prior methods which leverage formal differential privacy (DP) guarantees, while exhibiting comparable empirical privacy under strong attack models. Our results for the first time suggest that random projection can serve as a competitive and practical baseline for privacy-preserving RAG systems.
PDF arXiv
arXiv cs.AIOct 15
Multi-sourced AI Tutor for Non-Native English Speaker Students
Yiyin Shen, Xinyi Ai, Weibing Wang, Rogers Jeffrey Leo John, Meenakshi Syamkumar
Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student questions. With the rise in enrollment of international students in computing programs, researchers have begun to explore the difficulties that non-native English speaker (NNES) students face in computing courses due to language barriers. To aid such students, we developed a general-purpose AI tutor system that can process student questions in foreign languages (currently focused on Mandarin) and provide bilingual responses. Our system also incorporates the functionality of generating a glossary of technical terms and their explanations to enhance comprehension. Our approach utilizes a specialized Retrieval-Augmented Generation (RAG) pipeline with a novel question extraction mechanism, enabling us to effectively utilize a diverse array of course materials for answering student queries. We evaluated our AI tutor using 91 Piazza posts, demonstrating that it could answer 83.52% of the posts correctly and in a helpful manner. The tutor also provided high-quality translations for 76.92% of the questions and generated…
PDF arXiv
arXiv cs.AIOct 15
RAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
Mohammadreza Sediqin, Shlomo Engelson Argamon
In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a relation drawn from a closed inventory of functional categories. Neither label is a surface property: the same two clauses can be Elaboration or Background depending on rhetorical intent, and the boundary between them is fixed by annotation convention rather than by lexical cues. Predicting these labels therefore requires a formal discourse schema, and exposure to discourse-rich pretraining text does not supply it: given only a target EDU pair, a strong LLM recovers nuclearity at 0.794 F1 and relation at 0.738 F1. We propose RAG-RST, a retrieval-augmented framework that supplies this schema as in-context demonstration rather than through parameter updates, raising nuclearity to 0.846 F1 and relation to 0.788 F1 with no training. Two configurations are compared: a precomputed strategy that labels all EDU pairs before parsing, and a dynamic strategy that retrieves at each reduce step, so that the exemplars reaching the generator depend on the reductions the parser has already committed to. Both outperform existing RST parsers across…
PDF arXiv
arXiv cs.CLOct 15
Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning
Shiwen Qin, Cameron Barker, Henry Gouk, Elliot J. Crowley, Shay B. Cohen +2
Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty. However, context length alone cannot explain why models fail on long-context tasks that require reasoning over complex information. Contexts of equal length can pose very different challenges: a task may be solvable from a single local passage, or it may require integrating evidence distributed across the full context. We quantitatively characterise this difficulty through three properties: evidence scope, how much necessary information must be found; evidence dispersion, how widely that information is scattered within the context; and reasoning depth, how many dependent steps are needed to derive an answer from it. We construct LURCH, a multi-domain question-answering benchmark of 6,550 instances with supporting evidence and reasoning graphs, enabling all three properties to be quantified. Evaluations of proprietary and open-weight models show that: (1) at fixed context length, all our three properties are strongly negatively correlated with model performance; (2) as context length increases, performance degrades sharply on samples…
PDF arXiv
arXiv cs.AIOct 15
CARA: Causally Guided Retrieval Agent for Question Answering
Weicheng Liu, Xiangcheng Wu, Depeng Xu, Haopeng Zhang, Xi Niu
Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textual content. Existing agentic retrievers adaptively balance global search and multi-hop exploration but primarily rely on textual similarity to guide retrieval. We propose CARA, a training-free, CAusally guided Retrieval Agent that explicitly evaluates the causal relevance of retrieved evidence through two complementary criteria: consequentiality, measuring whether adding evidence improves answerability, and essentiality, assessing whether removing evidence weakens answerability. CARA integrates these assessments with path consistency to guide graph exploration and employs reciprocal rank fusion to aggregate evidence from multiple agents. We evaluate CARA on three STaRK datasets and augment STaRK with ground-truth answers to enable downstream question answering evaluation. Experimental results demonstrate that CARA consistently outperforms existing retrievers. CARA also improves answer quality and faithfulness while examining fewer graph nodes and requiring fewer retrieval operations. These findings demonstrate…
PDF arXiv
arXiv cs.CLOct 15
Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Owen Walters, Lihu Chen, Francesca Toni
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available. To study this problem, we construct temporal facts whose values changed after the models' training cutoffs. We evaluate one proprietary model under deployed web search and six open-weight model configurations under controlled retrieval. We find that models often answer from search snippets without opening the source pages, which is associated with higher rates of outdated answers and unfaithful citations. Our controlled experiments and layer-wise analysis reveal a gap between the availability of evidence and its effective use. Even when retrieved evidence influences internal predictions, models may still favour outdated information in their final answers. We hope these findings will help advance research on the reliable use of retrieved evidence in search-augmented LLMs.
PDF arXiv

Papers

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Coming with more sources: stars history

All tools

Unstructured4 releases this week
pgvector+17% downloads · 9.1M/wk+17.1%
MarkItDown+16% downloads · 3.9M/wk+16.3%
Chroma+15% downloads · 2.2M/wk+15.4%
GraphRAG+30% downloads · 16.2k/wk+29.7%

★ Spotlight this month

arXiv cs.CLCodeOct 14▲ 8

Latent Compaction with Test-Time Training

Yingfa Chen, Xingyu Shen, Chaojun Xiao, Xu Han

Long contexts enlarge key--value (KV) caches, increasing memory use and attention costs during generation. Compressing these caches must preserve information for unseen questions. We introduce **Latent Compaction**, which learns a compact document-specific cache through test-time training. We initialize cache entries using reconstruction importance, teacher attention, and key-space diversity. Training first fits attention outputs and log-normalizers to those of the full cache, with reconstruction-selected entries held fixed. It then jointly optimizes the entire cache through end-to-end distillation from the full-cache model. Model weights remain frozen throughout, and training uses only generic prompts about the document, without access to downstream questions or answers. Experiments on RULER, LongBench, and Phonebook Lookup demonstrate substantial gains over budget-matched compression baselines. On a RULER, for example, our method achieves 96.7% accuracy at 32$\times$ compression, compared with 40.0% for KVZip+. The method also transfers across model scales and families, with retrieval gains on both Qwen3 and MiniCPM5. These results show that treating the KV cache as a neural…

PDF arXiv page Details & code

Recent papers

All in Research →

arXiv cs.AIOct 15
Private Retrieval Augmented Generation via Random Projection
Dixi Yao, Tahseen Rabbani, Tian Li
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it can also introduce privacy risks by leaking sensitive information from the retrieval datastore. We propose a simple method to preserve datastore privacy in RAG systems via random projection. By applying the same projection to both datastore embeddings and query embeddings, our method provably preserves semantic similarity between queries and retrieved items while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets, we show that this lightweight approach achieves superior retrieval and generation performance compared to prior methods which leverage formal differential privacy (DP) guarantees, while exhibiting comparable empirical privacy under strong attack models. Our results for the first time suggest that random projection can serve as a competitive and practical baseline for privacy-preserving RAG systems.
PDF arXiv
arXiv cs.AIOct 15
Multi-sourced AI Tutor for Non-Native English Speaker Students
Yiyin Shen, Xinyi Ai, Weibing Wang, Rogers Jeffrey Leo John, Meenakshi Syamkumar
Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student questions. With the rise in enrollment of international students in computing programs, researchers have begun to explore the difficulties that non-native English speaker (NNES) students face in computing courses due to language barriers. To aid such students, we developed a general-purpose AI tutor system that can process student questions in foreign languages (currently focused on Mandarin) and provide bilingual responses. Our system also incorporates the functionality of generating a glossary of technical terms and their explanations to enhance comprehension. Our approach utilizes a specialized Retrieval-Augmented Generation (RAG) pipeline with a novel question extraction mechanism, enabling us to effectively utilize a diverse array of course materials for answering student queries. We evaluated our AI tutor using 91 Piazza posts, demonstrating that it could answer 83.52% of the posts correctly and in a helpful manner. The tutor also provided high-quality translations for 76.92% of the questions and generated…
PDF arXiv
arXiv cs.AIOct 15
RAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
Mohammadreza Sediqin, Shlomo Engelson Argamon
In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a relation drawn from a closed inventory of functional categories. Neither label is a surface property: the same two clauses can be Elaboration or Background depending on rhetorical intent, and the boundary between them is fixed by annotation convention rather than by lexical cues. Predicting these labels therefore requires a formal discourse schema, and exposure to discourse-rich pretraining text does not supply it: given only a target EDU pair, a strong LLM recovers nuclearity at 0.794 F1 and relation at 0.738 F1. We propose RAG-RST, a retrieval-augmented framework that supplies this schema as in-context demonstration rather than through parameter updates, raising nuclearity to 0.846 F1 and relation to 0.788 F1 with no training. Two configurations are compared: a precomputed strategy that labels all EDU pairs before parsing, and a dynamic strategy that retrieves at each reduce step, so that the exemplars reaching the generator depend on the reductions the parser has already committed to. Both outperform existing RST parsers across…
PDF arXiv
arXiv cs.CLOct 15
Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning
Shiwen Qin, Cameron Barker, Henry Gouk, Elliot J. Crowley, Shay B. Cohen +2
Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty. However, context length alone cannot explain why models fail on long-context tasks that require reasoning over complex information. Contexts of equal length can pose very different challenges: a task may be solvable from a single local passage, or it may require integrating evidence distributed across the full context. We quantitatively characterise this difficulty through three properties: evidence scope, how much necessary information must be found; evidence dispersion, how widely that information is scattered within the context; and reasoning depth, how many dependent steps are needed to derive an answer from it. We construct LURCH, a multi-domain question-answering benchmark of 6,550 instances with supporting evidence and reasoning graphs, enabling all three properties to be quantified. Evaluations of proprietary and open-weight models show that: (1) at fixed context length, all our three properties are strongly negatively correlated with model performance; (2) as context length increases, performance degrades sharply on samples…
PDF arXiv
arXiv cs.AIOct 15
CARA: Causally Guided Retrieval Agent for Question Answering
Weicheng Liu, Xiangcheng Wu, Depeng Xu, Haopeng Zhang, Xi Niu
Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textual content. Existing agentic retrievers adaptively balance global search and multi-hop exploration but primarily rely on textual similarity to guide retrieval. We propose CARA, a training-free, CAusally guided Retrieval Agent that explicitly evaluates the causal relevance of retrieved evidence through two complementary criteria: consequentiality, measuring whether adding evidence improves answerability, and essentiality, assessing whether removing evidence weakens answerability. CARA integrates these assessments with path consistency to guide graph exploration and employs reciprocal rank fusion to aggregate evidence from multiple agents. We evaluate CARA on three STaRK datasets and augment STaRK with ground-truth answers to enable downstream question answering evaluation. Experimental results demonstrate that CARA consistently outperforms existing retrievers. CARA also improves answer quality and faithfulness while examining fewer graph nodes and requiring fewer retrieval operations. These findings demonstrate…
PDF arXiv
arXiv cs.CLOct 15
Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Owen Walters, Lihu Chen, Francesca Toni
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available. To study this problem, we construct temporal facts whose values changed after the models' training cutoffs. We evaluate one proprietary model under deployed web search and six open-weight model configurations under controlled retrieval. We find that models often answer from search snippets without opening the source pages, which is associated with higher rates of outdated answers and unfaithful citations. Our controlled experiments and layer-wise analysis reveal a gap between the availability of evidence and its effective use. Even when retrieved evidence influences internal predictions, models may still favour outdated information in their final answers. We hope these findings will help advance research on the reliable use of retrieved evidence in search-augmented LLMs.
PDF arXiv

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 47m ago

RAG

Retrieval-augmented generation, retrieval for LLMs, long-context grounding and knowledge-base QA.

15papers this week

All papers · 15 With code · 1

Newest Most relevant

15 papers this month

Active contributors · 90 days

Dixi YaoPrivate Retrieval Augmented Generation via Random Projection1
Tahseen RabbaniPrivate Retrieval Augmented Generation via Random Projection1
Tian LiPrivate Retrieval Augmented Generation via Random Projection1
Meenakshi SyamkumarMulti-sourced AI Tutor for Non-Native English Speaker Students1
Rogers Jeffrey Leo JohnMulti-sourced AI Tutor for Non-Native English Speaker Students1
Weibing WangMulti-sourced AI Tutor for Non-Native English Speaker Students1

Open problems in RAG

Effective use of retrieved evidence in generation2 papers raise it · 2 address it
Models often ignore or misuse available evidence, leading to outdated or unfaithful answers despite retrieval.
Long-context reasoning difficulty beyond length1 paper raises it · 0 address it
Performance degrades when evidence is scattered, sparse, or requires multi-step reasoning, not just long context.
- Beyond Context Length: Characterising the Difficulty of Long-Context Reasoningraises
KV cache compression for long contexts1 paper raises it · 1 addresses it
Large KV caches increase memory and compute costs, but compression must preserve information for unseen queries.
- Latent Compaction with Test-Time Trainingraises · addresses
Privacy risks in RAG datastores1 paper raises it · 1 addresses it
Retrieval can leak sensitive information from external knowledge sources, requiring privacy-preserving methods.
- Private Retrieval Augmented Generation via Random Projectionraises · addresses
Visual evidence grounding in long documents1 paper raises it · 1 addresses it
Localizing and interpreting visual evidence (charts, tables) in long documents remains a bottleneck for QA systems.
- VELD-QA: A Large-Scale Dataset for Visual Evidence Grounding and Question Answering in Long Documentsraises · addresses

Named by an AI from these papers' abstracts.