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Vercel AI SDK300 releases this week

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Open problems in Frontier LLMs

Evaluating long-horizon agents in real-world web environments1 paper raises it · 1 addresses it
Current benchmarks do not test agents on live, open, and heterogeneous web tasks with complex interactions.
- Deep Search in Real-World Web Environmentsraises · addresses
Net value of agent memory over direct retrieval1 paper raises it · 1 addresses it
Many memory systems underperform naive retrieval, and their trade-offs are poorly understood.
- Memory Tax: Dissecting the Net Value of Agent Memoryraises · addresses
Inferring user habits from weak longitudinal evidence1 paper raises it · 1 addresses it
Agents struggle to learn and apply user habits from sparse or noisy historical interactions.
- HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memoryraises · addresses
Structural requirements for decision-native models1 paper raises it · 3 address it
Current decision models often lack null-awareness, permutation invariance, option scalability, and cacheability.
- 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
Efficient fact-checking of review comments1 paper raises it · 1 addresses it
Frontier LLMs are accurate but slow and expensive for checking implicit assertions in reviews.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Chaojinhua IntelligenceSeed · Oct 28undisclosed
Chaoqiumian TechnologySeed · Oct 26undisclosed

Recently funded

Topics in this field

retrieval augmented generation · knowledge graphs · retrieval augmented280 papers35 this week
language models llms · large language models · language models301 papers32 this week
memory construction · persistent memory · long term memory285 papers28 this week
large language models · machine translation · multilingual236 papers24 this week
generative recommendation · sequential recommendation · personalized171 papers22 this week
deep research · scientific literature · citations155 papers20 this week

Topics in this field

Active contributors · 90 days

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Activity · 12 weeks

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arXiv cs.SISep 30
Community-Driven API and AI Writer Design for Openly Scaling Community Notes
Brad Miller, Jay Baxter, Jiansong Chao, Keith Coleman, Sophie Hilgard +1
Community Notes is a crowd-sourced approach for adding context to posts on X. Contributors propose and rate notes, forming the inputs to an open-source, open-data algorithm that determines which notes show broadly to users. Since September 2025, Community Notes' AI Note Writer API has provided an open, public interface for using AI to propose notes, while adhering to the founding principle that users, not the platform or an AI, control which notes show on X. Explicit note requests and user posts on X determine the AI API post feeds, ensuring that AI note writing responds to demand from X users. We present the design, operation and impact of the AI API, including analysis of the interaction between AI and human generated notes across topics. Unless otherwise stated, measurements and system description reflect June 2-29, 2026. The Community Writer is the largest AI API client and contributes the bulk of AI API output, generating 52% of notes selected as Helpful and shown broadly on X. The writer is guided by community input during both training and operation to prioritize, draft, evaluate and delete proposed notes. Beyond scale, the writer also offers speed, submitting the first…
PDF arXiv
arXiv cs.CLSep 30
From Tweets to Trades: Analyzing the Influence of Public Mood over Stock Market Performance in Turkiye
Ece Elif Adak, Bertaç Şakir Şahin, Şaziye Betül Özateş
Purpose: This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across communication domains and market conditions. It distinguishes public mood from investor sentiment and investigates whether heterogeneous sources of public communication exhibit different relationships with market behaviour. Design: The study analyses 610,422 posts published by 176 curated X accounts between January 2022 and December 2023, covering Politics and Government, Economy and Finance, and Media and Society. Posts are classified using fine-tuned Turkish transformer models under three domain-specific and one pooled regime. Public mood measures are constructed at daily, weekly, and monthly frequencies and examined alongside BIST100 and BIST30 market measures using correlation, Granger causality, vector autoregression, and impulse response analyses across the full period and selected market conditions. Findings: Public mood is not associated with the direction of stock-market returns but is associated with the magnitude of price movements, particularly for Media and Society and pooled communication. These relationships become…
PDF arXiv
arXiv cs.CLSep 30
Jev-as-a-Judge for RAG Claim Verification: Confidence-Gated Cascading Between a Non-Generative Verifier and a Frontier LLM
Alden Do Rosario, Kirollos Atef
Retrieval-augmented generation asks one question at volume: does this evidence support this claim? The answer is one bit, yet most pipelines obtain it from a text generator and pay generation prices for the prose wrapped around it. We compare a non-generative decision model, which returns a typed value from a declared enumeration together with a native probability and no text, against a frontier LLM configured as an engineer would deploy it for binary classification. Both answer the same 495 human-labelled claims from LLM-AggreFact, so every comparison is within-item. We report three things. First, the two systems are statistically indistinguishable on the metric this task is conventionally scored by, a paired difference of -0.56 points, Jev minus Astra, at a 187-fold cost difference. Second, that tie carries almost no information about deployment: the systems disagree on 15.2% of claims, and the cheaper one admits 23.2% of the claims annotators marked unsupported against the frontier model's 14.1%. A component whose job is to catch unsupported claims should be judged on that rate, and the conventional headline hides it. Third, because the errors differ they compose: routing the…
PDF arXiv
arXiv cs.AISep 30
Better Deck or Different Judge? Evaluating Agentic Harness Gains in Corporate and Investment Banking
Ludovic Gibert, Matis Despujols, Andre-Louis Rochet
Corporate and investment banking teams use presentations to support credit decisions and advise clients on financing and transactions. Producing these decks requires reconciling financial data, tracing sources and turning analysis into a recommendation. We retrospectively study the development of an agentic harness combining a 27B language model, financial calculations, narrative templates and validation checks. LLM judges guide engineering changes and assess the resulting decks, raising the question of whether higher scores reflect better documents or changes in grading. In shared-session text-only grading with template markers removed, five judges score the complete system 20.4 to 33.6 points out of 95 above the same model generating directly from a short prompt. Every judge scores the system higher on all seventeen development deliverables. Margins against direct Opus generation from a short prompt range from -4.7 to +0.8 points. Judges agree on broad progress across development rounds but agree less on final-deck rankings than on pooled scores. Repeated grading also shifts scores on unchanged decks, making small improvements difficult to distinguish from judge variability.
PDF arXiv
arXiv cs.LGSep 30
RoPE at the End of Its Rope? Theory, Diagnosis, and Mitigation of Long-Context Failures
Yuyang Wu, Yufeng Du, Hao Peng
Long-context failures of RoPE-based language models can arise from RoPE's intrinsic tradeoff between maintaining stable token preferences and distinguishing nearby positions. Determining which weakness to address, and how, requires a more precise characterization of RoPE's behavior in trained models across context lengths. We address a key limitation of prior theory by allowing unequal query-key scales across RoPE frequencies, which aligns well with practical empirical observations. Our theory makes both vulnerabilities measurable for individual heads and inputs, and quantifies how high-frequency components support positional sensitivity while potentially disrupting semantic stability. We also derive a theoretical context-length bound beyond which, under specified conditions, a fixed attention-score comparison cannot jointly avoid semantic reversal and positional insensitivity. Guided by our fresh theoretical insights, we introduce RoPE Profiler, a lightweight, plug-and-play diagnostic toolkit that augments existing evaluations with zero additional forward passes by reusing cached query and key activations. Reusing activations collected during evaluation, the toolkit incurs…
PDF arXiv
arXiv cs.CLSep 30
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
Junyang Chen, Zecheng Wang, Jingbang Chen
Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompts on task examples and use the resulting feedback to guide prompt revisions through reflection. However, when feedback selection does not account for the prompt's weaknesses, these revisions may improve performance on selected examples without yielding broader task improvements. To address this issue, we propose AdaGEPA, an adaptive feedback-allocation method that uses the prompt's performance and task structure to select examples for the next prompt revision. Our method replaces at most one example in each feedback minibatch to target an identified weakness while preserving the remaining feedback context. Across our main experiments on six downstream benchmarks, AdaGEPA achieves higher mean validation scores than non-adaptive feedback selection under matched rollout budgets. AdaGEPA also finds high-performing prompts earlier across several tasks. In the initial Schema-Guided Dialogue (SGD) study, its half-budget prompts outperform the non-adaptive baseline's full-budget prompts in joint goal accuracy on new dialogues from services…
PDF arXiv

Papers

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All tools

jcode+233% downloads · 25.5k/wk+233.1%
billion-context+259% downloads · 1.1M/wk+258.8%
LMDeploy+136% downloads · 12.3k/wk+135.8%
oh-my-claudecode+47% downloads · 7.7k/wk+47.4%
Vercel AI SDK300 releases this week

Recently funded

Market

KineDBPre-seed · Oct 29$946.3k
Jingshuo TechnologySeed · Oct 29undisclosed
Kaola YouranSeries B+ · Oct 29undisclosed
Chaojinhua IntelligenceSeed · Oct 28undisclosed
Chaoqiumian TechnologySeed · Oct 26undisclosed

Topics in this field

retrieval augmented generation · knowledge graphs · retrieval augmented280 papers35 this week
language models llms · large language models · language models301 papers32 this week
memory construction · persistent memory · long term memory285 papers28 this week
large language models · machine translation · multilingual236 papers24 this week
generative recommendation · sequential recommendation · personalized171 papers22 this week
deep research · scientific literature · citations155 papers20 this week

Active contributors · 90 days

Activity · 12 weeks

Fields · AI · updated 48m ago

Frontier LLMs

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

274papers this week−45.3% vs last week

All papers · 2.6k With code · 74

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1.8k papers this month

Recent papers

All papers in Research

arXiv cs.SISep 30
Community-Driven API and AI Writer Design for Openly Scaling Community Notes
Brad Miller, Jay Baxter, Jiansong Chao, Keith Coleman, Sophie Hilgard +1
Community Notes is a crowd-sourced approach for adding context to posts on X. Contributors propose and rate notes, forming the inputs to an open-source, open-data algorithm that determines which notes show broadly to users. Since September 2025, Community Notes' AI Note Writer API has provided an open, public interface for using AI to propose notes, while adhering to the founding principle that users, not the platform or an AI, control which notes show on X. Explicit note requests and user posts on X determine the AI API post feeds, ensuring that AI note writing responds to demand from X users. We present the design, operation and impact of the AI API, including analysis of the interaction between AI and human generated notes across topics. Unless otherwise stated, measurements and system description reflect June 2-29, 2026. The Community Writer is the largest AI API client and contributes the bulk of AI API output, generating 52% of notes selected as Helpful and shown broadly on X. The writer is guided by community input during both training and operation to prioritize, draft, evaluate and delete proposed notes. Beyond scale, the writer also offers speed, submitting the first…
PDF arXiv
arXiv cs.CLSep 30
From Tweets to Trades: Analyzing the Influence of Public Mood over Stock Market Performance in Turkiye
Ece Elif Adak, Bertaç Şakir Şahin, Şaziye Betül Özateş
Purpose: This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across communication domains and market conditions. It distinguishes public mood from investor sentiment and investigates whether heterogeneous sources of public communication exhibit different relationships with market behaviour. Design: The study analyses 610,422 posts published by 176 curated X accounts between January 2022 and December 2023, covering Politics and Government, Economy and Finance, and Media and Society. Posts are classified using fine-tuned Turkish transformer models under three domain-specific and one pooled regime. Public mood measures are constructed at daily, weekly, and monthly frequencies and examined alongside BIST100 and BIST30 market measures using correlation, Granger causality, vector autoregression, and impulse response analyses across the full period and selected market conditions. Findings: Public mood is not associated with the direction of stock-market returns but is associated with the magnitude of price movements, particularly for Media and Society and pooled communication. These relationships become…
PDF arXiv
arXiv cs.CLSep 30
Jev-as-a-Judge for RAG Claim Verification: Confidence-Gated Cascading Between a Non-Generative Verifier and a Frontier LLM
Alden Do Rosario, Kirollos Atef
Retrieval-augmented generation asks one question at volume: does this evidence support this claim? The answer is one bit, yet most pipelines obtain it from a text generator and pay generation prices for the prose wrapped around it. We compare a non-generative decision model, which returns a typed value from a declared enumeration together with a native probability and no text, against a frontier LLM configured as an engineer would deploy it for binary classification. Both answer the same 495 human-labelled claims from LLM-AggreFact, so every comparison is within-item. We report three things. First, the two systems are statistically indistinguishable on the metric this task is conventionally scored by, a paired difference of -0.56 points, Jev minus Astra, at a 187-fold cost difference. Second, that tie carries almost no information about deployment: the systems disagree on 15.2% of claims, and the cheaper one admits 23.2% of the claims annotators marked unsupported against the frontier model's 14.1%. A component whose job is to catch unsupported claims should be judged on that rate, and the conventional headline hides it. Third, because the errors differ they compose: routing the…
PDF arXiv
arXiv cs.AISep 30
Better Deck or Different Judge? Evaluating Agentic Harness Gains in Corporate and Investment Banking
Ludovic Gibert, Matis Despujols, Andre-Louis Rochet
Corporate and investment banking teams use presentations to support credit decisions and advise clients on financing and transactions. Producing these decks requires reconciling financial data, tracing sources and turning analysis into a recommendation. We retrospectively study the development of an agentic harness combining a 27B language model, financial calculations, narrative templates and validation checks. LLM judges guide engineering changes and assess the resulting decks, raising the question of whether higher scores reflect better documents or changes in grading. In shared-session text-only grading with template markers removed, five judges score the complete system 20.4 to 33.6 points out of 95 above the same model generating directly from a short prompt. Every judge scores the system higher on all seventeen development deliverables. Margins against direct Opus generation from a short prompt range from -4.7 to +0.8 points. Judges agree on broad progress across development rounds but agree less on final-deck rankings than on pooled scores. Repeated grading also shifts scores on unchanged decks, making small improvements difficult to distinguish from judge variability.
PDF arXiv
arXiv cs.LGSep 30
RoPE at the End of Its Rope? Theory, Diagnosis, and Mitigation of Long-Context Failures
Yuyang Wu, Yufeng Du, Hao Peng
Long-context failures of RoPE-based language models can arise from RoPE's intrinsic tradeoff between maintaining stable token preferences and distinguishing nearby positions. Determining which weakness to address, and how, requires a more precise characterization of RoPE's behavior in trained models across context lengths. We address a key limitation of prior theory by allowing unequal query-key scales across RoPE frequencies, which aligns well with practical empirical observations. Our theory makes both vulnerabilities measurable for individual heads and inputs, and quantifies how high-frequency components support positional sensitivity while potentially disrupting semantic stability. We also derive a theoretical context-length bound beyond which, under specified conditions, a fixed attention-score comparison cannot jointly avoid semantic reversal and positional insensitivity. Guided by our fresh theoretical insights, we introduce RoPE Profiler, a lightweight, plug-and-play diagnostic toolkit that augments existing evaluations with zero additional forward passes by reusing cached query and key activations. Reusing activations collected during evaluation, the toolkit incurs…
PDF arXiv
arXiv cs.CLSep 30
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
Junyang Chen, Zecheng Wang, Jingbang Chen
Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompts on task examples and use the resulting feedback to guide prompt revisions through reflection. However, when feedback selection does not account for the prompt's weaknesses, these revisions may improve performance on selected examples without yielding broader task improvements. To address this issue, we propose AdaGEPA, an adaptive feedback-allocation method that uses the prompt's performance and task structure to select examples for the next prompt revision. Our method replaces at most one example in each feedback minibatch to target an identified weakness while preserving the remaining feedback context. Across our main experiments on six downstream benchmarks, AdaGEPA achieves higher mean validation scores than non-adaptive feedback selection under matched rollout budgets. AdaGEPA also finds high-performing prompts earlier across several tasks. In the initial Schema-Guided Dialogue (SGD) study, its half-budget prompts outperform the non-adaptive baseline's full-budget prompts in joint goal accuracy on new dialogues from services…
PDF arXiv

Open problems in Frontier LLMs

Evaluating long-horizon agents in real-world web environments1 paper raises it · 1 addresses it
Current benchmarks do not test agents on live, open, and heterogeneous web tasks with complex interactions.
- Deep Search in Real-World Web Environmentsraises · addresses
Net value of agent memory over direct retrieval1 paper raises it · 1 addresses it
Many memory systems underperform naive retrieval, and their trade-offs are poorly understood.
- Memory Tax: Dissecting the Net Value of Agent Memoryraises · addresses
Inferring user habits from weak longitudinal evidence1 paper raises it · 1 addresses it
Agents struggle to learn and apply user habits from sparse or noisy historical interactions.
- HABIT-Bench: Evaluating Habit Induction from Longitudinal Weak Evidence in Agent Memoryraises · addresses
Structural requirements for decision-native models1 paper raises it · 3 address it
Current decision models often lack null-awareness, permutation invariance, option scalability, and cacheability.
- 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
Efficient fact-checking of review comments1 paper raises it · 1 addresses it
Frontier LLMs are accurate but slow and expensive for checking implicit assertions in reviews.
- PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses

Named by an AI from these papers' abstracts.