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Open problems in Frontier LLMs

Evaluating and benchmarking decision models for reliability and calibration3 papers raise it · 4 address it
Decision models are increasingly used in LLM systems but lack standardized ways to measure correctness, confidence, and robustness across controlled variations.
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Efficient and accurate fact-checking of AI and human reviews1 paper raises it · 1 addresses it
Peer review systems struggle with factual errors in comments, and current methods are either too slow or inaccurate for large-scale use.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Long-horizon agent context management and memory retention2 papers raise it · 3 address it
Agents lose critical information when context windows are exceeded, and current compaction methods fail to preserve decision-relevant details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- Cortex: Consolidating Context into Parametric Memoryaddresses
Real-world web-based deep search and evidence integration1 paper raises it · 1 addresses it
Agents cannot reliably perform multi-step research in live, open web environments with diverse, interactive, and heterogeneous information sources.
- Deep Search in Real-World Web Environmentsraises · addresses
User behavior modeling for long-term personalization1 paper raises it · 1 addresses it
LLMs lack the ability to infer and adapt to evolving user preferences and intent from real-world behavior data over time.
- Vela: User Behavior as Supervision Sparks LLM Intelligenceraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Recently funded

Topics in this field

language models llms · large language models · language models276 papers50 this week
retrieval augmented generation · knowledge graphs · retrieval augmented250 papers39 this week
memory construction · persistent memory · long term memory263 papers38 this week
large language models · machine translation · multilingual218 papers29 this week
activation steering · attention heads · attention168 papers21 this week
generative recommendation · sequential recommendation · personalized150 papers21 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.CVOct 7
Global Average Precision for Representation Learning
Bill Psomas, Mohammad Mahdi, Michalis Thomas, Danda Pani Paudel, Giorgos Tolias +1
Standard information retrieval metrics, such as mean Average Precision (mAP), assess performance one query at a time, based on how the similarities between a query and its positives compare against those with its negatives. The same holds for common representation learning losses, such as InfoNCE and per-query AP surrogates. None of them considers whether similarities are comparable across queries, which any system with a single decision threshold relies on. Global Average Precision (gAP) does, by ranking all query-candidate pairs in one list and computing a single AP. We introduce gSAP, a differentiable surrogate of gAP. It needs only a similarity matrix and a binary matrix marking the positive pairs, the same input as existing losses, so it is a drop-in replacement for them and agnostic to the encoder, the modality, and the source of supervision. Since it considers all possible pairwise comparisons in the batch jointly, it also remains trainable at low temperatures, a regime where per-query surrogates run out of gradient. Swapping it into established recipes improves supervised metric learning, cross-modal alignment, and self-supervised pretraining, where, to our knowledge, it…
PDF arXiv
arXiv cs.CLOct 7
MIRROR: From Imitation to Internalization in LLM Personalization
Huayi Lai, Jicheng Yang, Min Yi, Chong Meng
The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap in existing fine-tuning paradigm. To address this limitation, we introduce MIRROR(Meta- personalization by Internalizing Reference-Revealed On-policy Reflections), a novel self-distillation framework that shifts LLM personalization from imitation toward preference internalization. First, we replace reference-token imitation with reference-revealed on-policy self-distillation, aligning the model's next-token distributions along its own generation trajectories with those of its reference-conditioned self, thereby internalizing user preferences rather than reproducing reference wording.Second, we introduce MIRROR-F, a focal plug-in that augments on-policy distributional alignment with selective supervision over informative reference tokens, thereby strengthening content generation while preserving user-specific expression. Across three personalized generation benchmarks, two model scales, and complementary reference-based and LLM-based evaluations, MIRROR and MIRROR-F achieve leading overall personalization performance and superior…
PDF arXiv
arXiv cs.CLOct 7
Decoupling Logic from Persona: Structural Immunity of Edge LLM Agents to Context Pollution
Masaaki Nakatsu, Reno Wang
Edge language-model agents must hold a persona and reason correctly at once, in one context window that fills with history and persona instructions. We study what happens to the logical part of such an agent when that history is long, misleading and persona-heavy (persona-logic interference), and present a Decoupling Architecture (AO-DA) that separates logical inference ("What") from persona expression ("How") into two inference paths on one INT4 base model with hot-swappable LoRA adapters. The logic path receives only the core turn and emits a verifiable structured state (Micro-State); the persona path renders it in character with the full history. In same-base-model ablations on an Apple M2 laptop (Llama-3.1-8B-Instruct and Gemma-3-4B-it, 4-bit; 480 runs: 2 bases x 4 pollution levels x 3 arms x 2 tasks x 2 personas x 5 seeds) we find: (i) the decoupled logic path is structurally invariant to pollution: its prompt stays at 180 (Llama) or 167 (Gemma) tokens while the mixed single-pass prompt grows from 242 to 1,203 on Llama, and its outputs are byte-identical across levels (40/40); (ii) the mixed single pass degrades monotonically (composite logic score 0.669 to 0.150 on Llama,…
PDF arXiv
arXiv cs.CLOct 7
Shaer: Controlled Arabic Poetry Generation with Meter Subform and Semantic Conditioning
Ahmad Abbas, Tamara Fakih, Nour Fakih, Ammar Mohanna
Classical Arabic poetry generation requires simultaneously satisfying semantic, linguistic, and fine-grained prosodic constraints. Existing systems typically control broad poetic attributes but do not jointly model semantic intent, meter subform, and poem length. We present Shaer, a controllable Classical Arabic poetry generation framework jointly conditioned on natural-language descriptions, meter subforms, and target hemistich counts. To support this task, we construct an enriched corpus of 116,032 classical Arabic poems derived from Ashaar, containing normalized meter-subform labels and automatically generated, validated semantic descriptions. We then adapt Yehia-7B using QLoRA-based supervised fine-tuning with a completion-only objective. Our evaluation combines automatic assessment of base-meter conformity, requested-subform adherence, and length control with three LLM judges, blinded human evaluation, and memorization analysis. Shaer achieves 95.17% base-meter accuracy, 91.75% poem-level meter-subform accuracy, and 83.40% exact count accuracy. Relative to its untuned foundation model, these results represent gains of 68.68, 57.77, and 38.93 percentage points, respectively;…
PDF arXiv
arXiv cs.CLOct 7
Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs
Seunghan Kim, Minyeong Choe, Hyunil Kim, Haehyun Cho
Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal circuit. We identify Bridge Routing Heads (BRH) in two large multilingual LLMs through a three-stage pipeline. The resulting language-specific head sets exhibit near-complete mutual exclusivity across the five languages, with a mean Jaccard similarity of only 0.017 for Llama 3.1 70B and 0.057 for Qwen 2.5 72B, revealing language-idiosyncratic circuits. Ablating general BRH increases two-hop Negative Log-Likelihood (NLL) by 39-89x the random-head baseline, providing direct causal evidence of their role. Amplifying these heads in a failing target-language pass rescues up to 51.7% of cross-lingual failures, with no training. The two models share this dual-circuit pattern but allocate heads differently: Llama concentrates chaining in a large general pool, while Qwen leans on larger language-specific pools. Together these results show that activation-level intervention alone can recover correct answers from cross-lingual reasoning failures.
PDF arXiv
arXiv cs.IROct 7
Towards Explaining Query Expansion Performance in Information Retrieval
Sourav Saha, Aditya Dutta, Soumajit Pramanik, Mandar Mitra
Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently outperforms others across all queries. This work seeks to explain the variation in QE performance through two complementary perspectives. The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model. The second is a separability perspective, which quantifies how distinctly relevant and non-relevant documents are scored for a given expanded query using Cohen's (d). We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness. Extensive experiments on the TREC Robust collection, TREC DL 2019-2022 passage collections, and TREC DL 2019-2020 document collections reveal several interesting patterns. In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval…
PDF arXiv

Papers

Recently funded

Market

OpenAIStage not given · Oct 22$30B
DeepSeekStage not given · Oct 22$15B
Flapping AirplanesSeries A · Oct 21$1B
Electric PlantSeed · Oct 20$11.2M
PennantSeed · Oct 20$5M

Top tools here

All tools

billion-context+259% downloads · 1.1M/wk+258.8%
jcode+233% downloads · 25.5k/wk+233.1%
TanStack AI+68% downloads · 1M/wk+67.7%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
NVIDIA Dynamo+83% downloads · 18.4k/wk+83.4%

Topics in this field

language models llms · large language models · language models276 papers50 this week
retrieval augmented generation · knowledge graphs · retrieval augmented250 papers39 this week
memory construction · persistent memory · long term memory263 papers38 this week
large language models · machine translation · multilingual218 papers29 this week
activation steering · attention heads · attention168 papers21 this week
generative recommendation · sequential recommendation · personalized150 papers21 this week

Active contributors · 90 days

Activity · 12 weeks

Open problems in Frontier LLMs

Evaluating and benchmarking decision models for reliability and calibration3 papers raise it · 4 address it
Decision models are increasingly used in LLM systems but lack standardized ways to measure correctness, confidence, and robustness across controlled variations.
- DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systemsraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
- Jeva: System One Models from First Principlesraises · addresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Efficient and accurate fact-checking of AI and human reviews1 paper raises it · 1 addresses it
Peer review systems struggle with factual errors in comments, and current methods are either too slow or inaccurate for large-scale use.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
Long-horizon agent context management and memory retention2 papers raise it · 3 address it
Agents lose critical information when context windows are exceeded, and current compaction methods fail to preserve decision-relevant details.
- REMORY: Learning Residual Memory for Context Compactionraises · addresses
- Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agentsraises · addresses
- Cortex: Consolidating Context into Parametric Memoryaddresses
Real-world web-based deep search and evidence integration1 paper raises it · 1 addresses it
Agents cannot reliably perform multi-step research in live, open web environments with diverse, interactive, and heterogeneous information sources.
- Deep Search in Real-World Web Environmentsraises · addresses
User behavior modeling for long-term personalization1 paper raises it · 1 addresses it
LLMs lack the ability to infer and adapt to evolving user preferences and intent from real-world behavior data over time.
- Vela: User Behavior as Supervision Sparks LLM Intelligenceraises · addresses

Named by an AI from these papers' abstracts.

Recent papers

All papers in Research

arXiv cs.CVOct 7
Global Average Precision for Representation Learning
Bill Psomas, Mohammad Mahdi, Michalis Thomas, Danda Pani Paudel, Giorgos Tolias +1
Standard information retrieval metrics, such as mean Average Precision (mAP), assess performance one query at a time, based on how the similarities between a query and its positives compare against those with its negatives. The same holds for common representation learning losses, such as InfoNCE and per-query AP surrogates. None of them considers whether similarities are comparable across queries, which any system with a single decision threshold relies on. Global Average Precision (gAP) does, by ranking all query-candidate pairs in one list and computing a single AP. We introduce gSAP, a differentiable surrogate of gAP. It needs only a similarity matrix and a binary matrix marking the positive pairs, the same input as existing losses, so it is a drop-in replacement for them and agnostic to the encoder, the modality, and the source of supervision. Since it considers all possible pairwise comparisons in the batch jointly, it also remains trainable at low temperatures, a regime where per-query surrogates run out of gradient. Swapping it into established recipes improves supervised metric learning, cross-modal alignment, and self-supervised pretraining, where, to our knowledge, it…
PDF arXiv
arXiv cs.CLOct 7
MIRROR: From Imitation to Internalization in LLM Personalization
Huayi Lai, Jicheng Yang, Min Yi, Chong Meng
The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap in existing fine-tuning paradigm. To address this limitation, we introduce MIRROR(Meta- personalization by Internalizing Reference-Revealed On-policy Reflections), a novel self-distillation framework that shifts LLM personalization from imitation toward preference internalization. First, we replace reference-token imitation with reference-revealed on-policy self-distillation, aligning the model's next-token distributions along its own generation trajectories with those of its reference-conditioned self, thereby internalizing user preferences rather than reproducing reference wording.Second, we introduce MIRROR-F, a focal plug-in that augments on-policy distributional alignment with selective supervision over informative reference tokens, thereby strengthening content generation while preserving user-specific expression. Across three personalized generation benchmarks, two model scales, and complementary reference-based and LLM-based evaluations, MIRROR and MIRROR-F achieve leading overall personalization performance and superior…
PDF arXiv
arXiv cs.CLOct 7
Decoupling Logic from Persona: Structural Immunity of Edge LLM Agents to Context Pollution
Masaaki Nakatsu, Reno Wang
Edge language-model agents must hold a persona and reason correctly at once, in one context window that fills with history and persona instructions. We study what happens to the logical part of such an agent when that history is long, misleading and persona-heavy (persona-logic interference), and present a Decoupling Architecture (AO-DA) that separates logical inference ("What") from persona expression ("How") into two inference paths on one INT4 base model with hot-swappable LoRA adapters. The logic path receives only the core turn and emits a verifiable structured state (Micro-State); the persona path renders it in character with the full history. In same-base-model ablations on an Apple M2 laptop (Llama-3.1-8B-Instruct and Gemma-3-4B-it, 4-bit; 480 runs: 2 bases x 4 pollution levels x 3 arms x 2 tasks x 2 personas x 5 seeds) we find: (i) the decoupled logic path is structurally invariant to pollution: its prompt stays at 180 (Llama) or 167 (Gemma) tokens while the mixed single-pass prompt grows from 242 to 1,203 on Llama, and its outputs are byte-identical across levels (40/40); (ii) the mixed single pass degrades monotonically (composite logic score 0.669 to 0.150 on Llama,…
PDF arXiv
arXiv cs.CLOct 7
Shaer: Controlled Arabic Poetry Generation with Meter Subform and Semantic Conditioning
Ahmad Abbas, Tamara Fakih, Nour Fakih, Ammar Mohanna
Classical Arabic poetry generation requires simultaneously satisfying semantic, linguistic, and fine-grained prosodic constraints. Existing systems typically control broad poetic attributes but do not jointly model semantic intent, meter subform, and poem length. We present Shaer, a controllable Classical Arabic poetry generation framework jointly conditioned on natural-language descriptions, meter subforms, and target hemistich counts. To support this task, we construct an enriched corpus of 116,032 classical Arabic poems derived from Ashaar, containing normalized meter-subform labels and automatically generated, validated semantic descriptions. We then adapt Yehia-7B using QLoRA-based supervised fine-tuning with a completion-only objective. Our evaluation combines automatic assessment of base-meter conformity, requested-subform adherence, and length control with three LLM judges, blinded human evaluation, and memorization analysis. Shaer achieves 95.17% base-meter accuracy, 91.75% poem-level meter-subform accuracy, and 83.40% exact count accuracy. Relative to its untuned foundation model, these results represent gains of 68.68, 57.77, and 38.93 percentage points, respectively;…
PDF arXiv
arXiv cs.CLOct 7
Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs
Seunghan Kim, Minyeong Choe, Hyunil Kim, Haehyun Cho
Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal circuit. We identify Bridge Routing Heads (BRH) in two large multilingual LLMs through a three-stage pipeline. The resulting language-specific head sets exhibit near-complete mutual exclusivity across the five languages, with a mean Jaccard similarity of only 0.017 for Llama 3.1 70B and 0.057 for Qwen 2.5 72B, revealing language-idiosyncratic circuits. Ablating general BRH increases two-hop Negative Log-Likelihood (NLL) by 39-89x the random-head baseline, providing direct causal evidence of their role. Amplifying these heads in a failing target-language pass rescues up to 51.7% of cross-lingual failures, with no training. The two models share this dual-circuit pattern but allocate heads differently: Llama concentrates chaining in a large general pool, while Qwen leans on larger language-specific pools. Together these results show that activation-level intervention alone can recover correct answers from cross-lingual reasoning failures.
PDF arXiv
arXiv cs.IROct 7
Towards Explaining Query Expansion Performance in Information Retrieval
Sourav Saha, Aditya Dutta, Soumajit Pramanik, Mandar Mitra
Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently outperforms others across all queries. This work seeks to explain the variation in QE performance through two complementary perspectives. The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model. The second is a separability perspective, which quantifies how distinctly relevant and non-relevant documents are scored for a given expanded query using Cohen's (d). We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness. Extensive experiments on the TREC Robust collection, TREC DL 2019-2022 passage collections, and TREC DL 2019-2020 document collections reveal several interesting patterns. In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval…
PDF arXiv

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