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Open problems in Frontier LLMs

Structural decision-making in LLMs1 paper raises it · 3 address it
Current LLMs lack native support for decision problems with structural requirements like null-awareness, permutation invariance, and scalability.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsaddresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Adaptive computation for decision models1 paper raises it · 1 addresses it
Fixed-depth decision models waste computation on easy questions and cannot scale depth dynamically.
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
Reliable no-thinking behavior in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or misinterpret no-thinking instructions, leading to unwanted reasoning traces.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
Memory consolidation in context-augmented LLMs1 paper raises it · 1 addresses it
Gains from context are lost after its removal, and models struggle to retain learned knowledge without forgetting.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-generated training data degrades over rounds due to invalid or repetitive questions, causing performance collapse.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

reasoning traces · reasoning models · latent reasoning372 papers68 this week
dense retrieval · retrieval augmented generation · retrieval246 papers45 this week
attention heads · large language models · language models218 papers45 this week
large language models · attribution · language models llms196 papers37 this week
large language models · large language · language models178 papers34 this week
typed decision models · typed decision model · typed decisions129 papers32 this week

Topics in this field

Active contributors · 90 days

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arXiv cs.AIOct 19
StrucChunk: Structure-Aware Retrieval for Legal Document RAG
Surya Deep Singh, Ashutosh Tripathi, Gaurav Deep Singh
Retrieval-Augmented Generation (RAG) can ground LLM outputs in domain-specific knowledge, but legal documents are hard: standard chunking fragments hierarchical argument structure, and generic embeddings struggle with both precise citation matching and semantic understanding. We propose StrucChunk (Structure Aware Chunking) with hybrid dense-sparse retrieval for hierarchically structured legal documents. It treats documents as directed acyclic graphs of provisions, preserves clause boundaries, resolves cross-references before embedding, and combines BM25 with BGE-M3 via Reciprocal Rank Fusion. We evaluate on five corpora, three legal traditions and two languages. StrucChunk reaches 100% boundary precision (vs. 7.7% for recursive splitting on CrPC), preserves 97.6% of documented cross-references (vs. 0%), and matches section coverage with 40.3% fewer chunks on GDPR. On LegalBench-RAG (6,858 query-passage pairs), it achieves Char-Recall@8 of 48.18% vs. 23.59% for the best baseline (+104%), surpassing the published state of the art by +83%. On 501 queries each for CrPC and CPC (Indian law), Recall@5 improves from 0.557 to 0.804 (+44.4%) and from 0.158 to 0.259 (+64.6%); chunking…
PDF arXiv
arXiv cs.AIOct 19
Machina Pauliana: A neuro-symbolic approach to the actio pauliana in Dutch insolvency law
Daan Boven, Gregor Behnke, David Graus
Applying a legal provision frequently requires both structured rule-based reasoning and sensitivity to open-textured terms developed in case law. The actio pauliana of Article 42 of the Dutch Bankruptcy Act (Faillissementswet, Fw), which allows an insolvency practitioner to avoid transactions that prejudice creditors, requires both. We present a neuro-symbolic model of Article 42 Fw. Its symbolic core is written in Catala in a literate style that keeps statute and formalisation aligned and was developed through pair programming, with open-textured terms represented as boolean inputs where neural components couple to the symbolic solver. A neural routine structures a case's natural-language facts into boolean and date inputs. Neural subroutines resolve the open-textured terms through case-based reasoning over Dutch civil judgments, retrieved through a pipeline that filters on the requirement being resolved and expands along the procedural chain of a case. Segmenting and labelling those judgments attains high precision and recall against a manually annotated gold standard of 100 judgments. On ten hand-annotated cases the neuro-symbolic model handles most criteria reasonably but…
PDF arXiv
arXiv cs.CLOct 19
Shaping the Document, Not the Model: Optimized Suffixes for Context Following
Gia-Bao Dinh Ho, Sekh Mainul Islam, Masahiro Kaneko, Timothy Baldwin, Isabelle Augenstein
When a user supplies a document and wants the answer drawn from it, a language model is expected to follow it even when it contradicts its parametric knowledge. Prior work has focused on making the model rely on context by steering activations, contrasting logits during decoding, or instructing it in plain language. We ask whether the document alone, with no instruction written into it and no access to the model at inference time, is enough. We optimize a document suffix by discrete token search, starting from the answer to a single question and extending the objective to a set of passages, to multi-hop questions that follow from the planted fact, and to later turns of a conversation. On counterfactual passages that contradict parametric knowledge of the model, the suffix flips nearly all items the model resists across three instruction-tuned model families, in most settings outperforms the decoding baseline and the strongest instruction we could write, and matches activation steering. Controls show that the suffix largely acts through the passage rather than injecting the answer, and a single suffix per model transfers to unseen passages. Each extended objective carries the…
PDF arXiv
arXiv cs.AIOct 19
Towards More Granular Reasoning: Mapping the DSA Transparency Database Taxonomy to Platform Terms and Conditions Using Large Language Models
Alyson Ranucci, Eline Leijten, David Graus
Article 24(5) of the Digital Services Act (DSA) requires providers of online platforms to submit every content moderation decision they make, with a corresponding Statement of Reasons (SoR), to the DSA Transparency Database (DSA-TDB). Such decisions are classified against seventeen high-level attribute categories that categorize the primary reason for the moderation decision. Because over 99.8% of statements cite a violation of the provider's own terms and conditions rather than a legal provision, the database is overwhelmingly a record of contractual enforcement using catch-all reporting; however, whether its categories actually describe those contractual rules well has not been examined. In this paper, we map the terms and conditions (T&C) of 31 designated online services against the DSA-TDB's high-level category taxonomy using a few-shot GPT-4.1 classifier over 447 policy documents. The analysis concerns how T&C rules are represented in the taxonomy, not how they are enforced. Two findings emerge. First, high-level category applicability varies sharply by service type: unsafe products account for 17.5% of e-commerce assignments and 0.2% of adult-platform assignments, and…
PDF arXiv
arXiv cs.CLOct 19
Rethinking BPE: Merge Selection as Model Selection
Vijini Liyanage, François Yvon
Subword tokenization methods such as Byte-Pair Encoding (BPE) are a core component of modern language models; yet, tokenizer capacity remains typically determined by a fixed merge budget, chosen heuristically. Since larger vocabularies increase memory usage and computational cost, selecting compact and effective token inventories is essential for efficient language modeling. We study the optimization of a BPE merge budget as a statistical model selection problem and investigate information-theoretic criteria for \emph{computing optimal merge sets} directly from corpus statistics. To further improve the merge selection in limited data conditions, we augment the BPE training corpora with synthetic texts generated from the original data, providing estimates for the unigram and bigram distributions. Experiments across multiple corpora and downstream tasks show that using a principled vocabulary selection technique reduces model size, with little degradation in performance. We further observe that synthetic data can yield better tokenizers, especially in low-resource settings.
PDF arXiv
arXiv cs.AIOct 19
LLMs Prompted for Legal Context Refuse More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
Anastasiia Kucherenko, Francois Brouchoud, Dimitri Percia David, Andrei Kucharavy
Confidentiality requirements increasingly push legal professionals toward small, on-device LLMs that keep case data in-house, used for everyday tasks such as summarization, translation, and reformulation. Following standard prompting guidance, a user naturally states their role (``I am a lawyer...''). We show that this innocuous habit has an unexpected cost: such authority framing can make the model selectively refuse legitimate requests, introducing bias through uneven access to assistance. We evaluate four modern small open-weight LLMs on benign legal-domain prompts to measure this over-refusal. Surprisingly, we find that authority-style prefixes (''I am legal counsel working for a national supreme court'', ''I'm a defense lawyer'') systematically increase refusal rates by 2--20x over the no-prefix baseline, while a known role-play jailbreak prefix shows mixed effects, sharply increasing refusals in some models and barely shifting them in others. The finding suggests that small on-prem deployable LLMs are unstable under contextual framings that a real institutional user might naturally introduce, and further investigation is essential to minimize opportunities for bias.
PDF arXiv

Papers

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All tools

NVIDIA Dynamo+60% downloads · 15.8k/wk+60.0%
Agno+17% downloads · 519.1k/wk+16.8%
BentoML+30% downloads · 41k/wk+29.7%
n8n+25% downloads · 148.3k/wk+25.0%
LangGraph7 releases this week

Recently funded

Market

InfinigenceSeries B+ · Oct 12$343.6M
Lean SuperIntelligencePre-seed · Oct 9undisclosed
VinciSeries B+ · Oct 6$250M
Siena AISeries A · Oct 6$17M
Ocular AIPre-seed · Oct 5$2M

Topics in this field

reasoning traces · reasoning models · latent reasoning372 papers68 this week
dense retrieval · retrieval augmented generation · retrieval246 papers45 this week
attention heads · large language models · language models218 papers45 this week
large language models · attribution · language models llms196 papers37 this week
large language models · large language · language models178 papers34 this week
typed decision models · typed decision model · typed decisions129 papers32 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.AIOct 19
StrucChunk: Structure-Aware Retrieval for Legal Document RAG
Surya Deep Singh, Ashutosh Tripathi, Gaurav Deep Singh
Retrieval-Augmented Generation (RAG) can ground LLM outputs in domain-specific knowledge, but legal documents are hard: standard chunking fragments hierarchical argument structure, and generic embeddings struggle with both precise citation matching and semantic understanding. We propose StrucChunk (Structure Aware Chunking) with hybrid dense-sparse retrieval for hierarchically structured legal documents. It treats documents as directed acyclic graphs of provisions, preserves clause boundaries, resolves cross-references before embedding, and combines BM25 with BGE-M3 via Reciprocal Rank Fusion. We evaluate on five corpora, three legal traditions and two languages. StrucChunk reaches 100% boundary precision (vs. 7.7% for recursive splitting on CrPC), preserves 97.6% of documented cross-references (vs. 0%), and matches section coverage with 40.3% fewer chunks on GDPR. On LegalBench-RAG (6,858 query-passage pairs), it achieves Char-Recall@8 of 48.18% vs. 23.59% for the best baseline (+104%), surpassing the published state of the art by +83%. On 501 queries each for CrPC and CPC (Indian law), Recall@5 improves from 0.557 to 0.804 (+44.4%) and from 0.158 to 0.259 (+64.6%); chunking…
PDF arXiv
arXiv cs.AIOct 19
Machina Pauliana: A neuro-symbolic approach to the actio pauliana in Dutch insolvency law
Daan Boven, Gregor Behnke, David Graus
Applying a legal provision frequently requires both structured rule-based reasoning and sensitivity to open-textured terms developed in case law. The actio pauliana of Article 42 of the Dutch Bankruptcy Act (Faillissementswet, Fw), which allows an insolvency practitioner to avoid transactions that prejudice creditors, requires both. We present a neuro-symbolic model of Article 42 Fw. Its symbolic core is written in Catala in a literate style that keeps statute and formalisation aligned and was developed through pair programming, with open-textured terms represented as boolean inputs where neural components couple to the symbolic solver. A neural routine structures a case's natural-language facts into boolean and date inputs. Neural subroutines resolve the open-textured terms through case-based reasoning over Dutch civil judgments, retrieved through a pipeline that filters on the requirement being resolved and expands along the procedural chain of a case. Segmenting and labelling those judgments attains high precision and recall against a manually annotated gold standard of 100 judgments. On ten hand-annotated cases the neuro-symbolic model handles most criteria reasonably but…
PDF arXiv
arXiv cs.CLOct 19
Shaping the Document, Not the Model: Optimized Suffixes for Context Following
Gia-Bao Dinh Ho, Sekh Mainul Islam, Masahiro Kaneko, Timothy Baldwin, Isabelle Augenstein
When a user supplies a document and wants the answer drawn from it, a language model is expected to follow it even when it contradicts its parametric knowledge. Prior work has focused on making the model rely on context by steering activations, contrasting logits during decoding, or instructing it in plain language. We ask whether the document alone, with no instruction written into it and no access to the model at inference time, is enough. We optimize a document suffix by discrete token search, starting from the answer to a single question and extending the objective to a set of passages, to multi-hop questions that follow from the planted fact, and to later turns of a conversation. On counterfactual passages that contradict parametric knowledge of the model, the suffix flips nearly all items the model resists across three instruction-tuned model families, in most settings outperforms the decoding baseline and the strongest instruction we could write, and matches activation steering. Controls show that the suffix largely acts through the passage rather than injecting the answer, and a single suffix per model transfers to unseen passages. Each extended objective carries the…
PDF arXiv
arXiv cs.AIOct 19
Towards More Granular Reasoning: Mapping the DSA Transparency Database Taxonomy to Platform Terms and Conditions Using Large Language Models
Alyson Ranucci, Eline Leijten, David Graus
Article 24(5) of the Digital Services Act (DSA) requires providers of online platforms to submit every content moderation decision they make, with a corresponding Statement of Reasons (SoR), to the DSA Transparency Database (DSA-TDB). Such decisions are classified against seventeen high-level attribute categories that categorize the primary reason for the moderation decision. Because over 99.8% of statements cite a violation of the provider's own terms and conditions rather than a legal provision, the database is overwhelmingly a record of contractual enforcement using catch-all reporting; however, whether its categories actually describe those contractual rules well has not been examined. In this paper, we map the terms and conditions (T&C) of 31 designated online services against the DSA-TDB's high-level category taxonomy using a few-shot GPT-4.1 classifier over 447 policy documents. The analysis concerns how T&C rules are represented in the taxonomy, not how they are enforced. Two findings emerge. First, high-level category applicability varies sharply by service type: unsafe products account for 17.5% of e-commerce assignments and 0.2% of adult-platform assignments, and…
PDF arXiv
arXiv cs.CLOct 19
Rethinking BPE: Merge Selection as Model Selection
Vijini Liyanage, François Yvon
Subword tokenization methods such as Byte-Pair Encoding (BPE) are a core component of modern language models; yet, tokenizer capacity remains typically determined by a fixed merge budget, chosen heuristically. Since larger vocabularies increase memory usage and computational cost, selecting compact and effective token inventories is essential for efficient language modeling. We study the optimization of a BPE merge budget as a statistical model selection problem and investigate information-theoretic criteria for \emph{computing optimal merge sets} directly from corpus statistics. To further improve the merge selection in limited data conditions, we augment the BPE training corpora with synthetic texts generated from the original data, providing estimates for the unigram and bigram distributions. Experiments across multiple corpora and downstream tasks show that using a principled vocabulary selection technique reduces model size, with little degradation in performance. We further observe that synthetic data can yield better tokenizers, especially in low-resource settings.
PDF arXiv
arXiv cs.AIOct 19
LLMs Prompted for Legal Context Refuse More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context
Anastasiia Kucherenko, Francois Brouchoud, Dimitri Percia David, Andrei Kucharavy
Confidentiality requirements increasingly push legal professionals toward small, on-device LLMs that keep case data in-house, used for everyday tasks such as summarization, translation, and reformulation. Following standard prompting guidance, a user naturally states their role (``I am a lawyer...''). We show that this innocuous habit has an unexpected cost: such authority framing can make the model selectively refuse legitimate requests, introducing bias through uneven access to assistance. We evaluate four modern small open-weight LLMs on benign legal-domain prompts to measure this over-refusal. Surprisingly, we find that authority-style prefixes (''I am legal counsel working for a national supreme court'', ''I'm a defense lawyer'') systematically increase refusal rates by 2--20x over the no-prefix baseline, while a known role-play jailbreak prefix shows mixed effects, sharply increasing refusals in some models and barely shifting them in others. The finding suggests that small on-prem deployable LLMs are unstable under contextual framings that a real institutional user might naturally introduce, and further investigation is essential to minimize opportunities for bias.
PDF arXiv

Activity · 12 weeks

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Fields · AI · updated 17m ago

Frontier LLMs

The largest general-purpose language models, their releases, capabilities, scaling and training at frontier scale.

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2.1k papers this month

Open problems in Frontier LLMs

Structural decision-making in LLMs1 paper raises it · 3 address it
Current LLMs lack native support for decision problems with structural requirements like null-awareness, permutation invariance, and scalability.
- Jeva: System One Models from First Principlesraises · addresses
- LEV: Thinking Fast and Deep with Looped Decision Modelsaddresses
- SearchJev: A Fast and Calibrated System-1 Model for Search Agentsaddresses
Adaptive computation for decision models1 paper raises it · 1 addresses it
Fixed-depth decision models waste computation on easy questions and cannot scale depth dynamically.
- LEV: Thinking Fast and Deep with Looped Decision Modelsraises · addresses
Reliable no-thinking behavior in LLMs1 paper raises it · 1 addresses it
LLMs often ignore or misinterpret no-thinking instructions, leading to unwanted reasoning traces.
- Thinking Inertia: LLMs Keep Thinking When Told Not Toraises · addresses
Memory consolidation in context-augmented LLMs1 paper raises it · 1 addresses it
Gains from context are lost after its removal, and models struggle to retain learned knowledge without forgetting.
- Cortex: Consolidating Context into Parametric Memoryraises · addresses
Sustaining self-evolution in reasoning models1 paper raises it · 1 addresses it
Self-generated training data degrades over rounds due to invalid or repetitive questions, causing performance collapse.
- Questioning the Questions: Sustaining Self-Evolution in Reasoning Modelsraises · addresses

Named by an AI from these papers' abstracts.