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arxiv: 2605.16358 · v1 · pith:LCSWSEXNnew · submitted 2026-05-09 · 💻 cs.LG · cs.AI

LEAF: A Living Benchmark for Event-Augmented Forecasting

Mingtian Tan , Mihir Parmar , Palash Goyal , Chun-Liang Li , Nanyun Peng , Thomas Hartvigsen , Jinsung Yoon , Tomas Pfister This is my paper

Pith reviewed 2026-05-20 22:14 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords living benchmarkevent-augmented forecastinglarge language modelstime series forecastingstock predictionretrieval agentspredictive performance
0
0 comments X

The pith

LLMs can leverage signals from complex events to enhance forecasting performance in the LEAF living benchmark.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes LEAF as the first living benchmark for event-augmented forecasting tasks including future event probabilities, trends, and time series. It relies on a recursive retrieval agent system with dual-agent cross-validation to generate auxiliary text that avoids pre-training contamination while supplying relevant real-world events. Evaluations of state-of-the-art LLMs show these models improve predictions by extracting signals from the events, with stronger results on stocks the models rate as more predictable and a clear correlation between the events and target equities. This setup addresses limitations of prior benchmarks that either lack multidimensional events or operate in closed environments.

Core claim

LEAF supplies a dynamically updating testbed in which LLMs demonstrate the capacity to improve forecasting by incorporating signals from complex events, achieving better results on equities identified as predictable and revealing strong event-equity correlations.

What carries the argument

Recursive retrieval agent system paired with dual-agent cross-validation that supplies comprehensive, relevant, and uncontaminated auxiliary text for the forecasting tasks.

If this is right

  • LLMs achieve better performance on equities they confidently identify as more predictable.
  • The events demonstrate a strong correlation with the target equities.
  • LEAF provides a necessary, dynamically updating testbed to continuously track and drive progress in event-driven forecasting tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same retrieval approach could support event-augmented forecasting in domains beyond finance such as climate trends or public health indicators.
  • Strong event correlations may point to LLMs surfacing leading indicators that simpler statistical models overlook.
  • Re-running the benchmark on historical events isolated from recent data could test whether the observed gains depend on current information access.

Load-bearing premise

The recursive retrieval agent system paired with dual-agent cross-validation supplies comprehensive, relevant, and uncontaminated auxiliary text that genuinely improves forecasting without introducing selection bias or leakage.

What would settle it

Compare LLM forecasting accuracy on the same targets with and without the event-derived auxiliary text; if gains disappear or correlations weaken substantially, the claim that event signals enhance performance would not hold.

read the original abstract

Large Language Models (LLMs) are increasingly applied to forecasting. To evaluate this capability while mitigating pre-training data contamination, several living benchmarks have been proposed. However, existing benchmarks either lack the multidimensional events essential for accurate forecasting due to data scarcity, or focus on relatively closed environments. To assess the predictive capabilities of LLMs in complex, real-world scenarios, we propose LEAF, the first living benchmark for event-augmented forecasting tasks, including future event probabilities, trend and time series forecasting. LEAF utilizes a recursive retrieval agent system paired with dual-agent cross-validation to provide comprehensive and relevant auxiliary text for forecasting. Evaluating state-of-the-art proprietary and open-weight LLMs, we find that these models can leverage signals extracted from complex events to enhance predictive performance. In the stock domain, we find that LLMs achieve better performance on equities they confidently identify as more predictable. Furthermore, the events demonstrate a strong correlation with the target equities. To this end, LEAF provides a necessary, dynamically updating testbed to continuously track and drive progress in event-driven forecasting tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes LEAF, the first living benchmark for event-augmented forecasting tasks (future event probabilities, trend forecasting, and time series forecasting). It introduces a recursive retrieval agent system paired with dual-agent cross-validation to supply comprehensive auxiliary event text, evaluates state-of-the-art proprietary and open-weight LLMs on this benchmark, and reports that the models leverage complex event signals to improve predictive performance, achieve better results on equities they identify as more predictable, and exhibit strong event-target correlations.

Significance. If the auxiliary text generation is shown to be free of temporal leakage and selection bias, LEAF could serve as a valuable dynamic testbed that addresses gaps in prior living benchmarks by incorporating multidimensional real-world events, thereby enabling more rigorous tracking of progress in event-driven LLM forecasting.

major comments (2)
  1. [Methods (recursive retrieval agent system and dual-agent cross-validation)] The central claim that LLMs enhance forecasting by leveraging signals from complex events rests on the recursive retrieval agent and dual-agent cross-validation delivering uncontaminated, causally relevant text. The manuscript does not specify how retrieval is strictly time-bounded to exclude any events after the forecast horizon or how the dual-agent filter avoids favoring events that correlate with realized outcomes post hoc.
  2. [Evaluation and results] The reported performance improvements, better results on confidently predictable equities, and strong event correlations are presented without accompanying quantitative metrics, error bars, dataset sizes, ablation studies isolating the contribution of the auxiliary text, or baseline comparisons in the evaluation sections.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one concrete performance delta or correlation coefficient to convey the scale of the empirical findings.
  2. [Methods] Clarify the precise criteria used by the dual-agent cross-validation to accept or reject retrieved events, including any inter-agent agreement threshold.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has helped us strengthen the presentation of LEAF. We respond to each major comment below and have made corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Methods (recursive retrieval agent system and dual-agent cross-validation)] The central claim that LLMs enhance forecasting by leveraging signals from complex events rests on the recursive retrieval agent and dual-agent cross-validation delivering uncontaminated, causally relevant text. The manuscript does not specify how retrieval is strictly time-bounded to exclude any events after the forecast horizon or how the dual-agent filter avoids favoring events that correlate with realized outcomes post hoc.

    Authors: We agree that the original description of these mechanisms could have been more explicit to fully support the central claim. In the revised manuscript we have added a dedicated paragraph in the Methods section that specifies the temporal constraints: the recursive retrieval agent issues queries exclusively against events whose timestamps precede the forecast horizon, enforced via database-level timestamp filters with no post-horizon data accessible to the agent. For the dual-agent cross-validation, we now clarify that both agents are restricted to pre-horizon information; the first agent scores events by semantic relevance to the target query alone, while the second agent performs validation exclusively on historical hold-out periods that contain no information about realized future outcomes, thereby preventing post-hoc selection bias. revision: yes

  2. Referee: [Evaluation and results] The reported performance improvements, better results on confidently predictable equities, and strong event correlations are presented without accompanying quantitative metrics, error bars, dataset sizes, ablation studies isolating the contribution of the auxiliary text, or baseline comparisons in the evaluation sections.

    Authors: We accept that the evaluation section would benefit from greater quantitative rigor. The revised manuscript now augments the Evaluation and Results sections with concrete performance metrics (including accuracy and correlation coefficients), error bars obtained from repeated trials, explicit dataset sizes (number of equities and events processed), ablation experiments that remove the auxiliary event text, and direct comparisons against non-augmented LLM baselines as well as conventional time-series forecasting models. These additions provide the requested quantitative grounding for the reported improvements and correlations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with no derivation chain

full rationale

The paper proposes LEAF as a living benchmark and reports empirical evaluations of LLMs on event-augmented forecasting tasks. No mathematical derivations, fitted parameters, or equations are presented that could reduce a claimed prediction to an input by construction. The recursive retrieval and dual-agent system is described as a data-collection method rather than a self-referential definition, and results are framed as experimental findings rather than outputs forced by prior self-citations or ansatzes. The work is self-contained against external benchmarks and does not rely on load-bearing self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The proposal rests on domain assumptions about data scarcity in existing benchmarks and the effectiveness of the agent pipeline; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Existing benchmarks either lack multidimensional events due to data scarcity or focus on relatively closed environments.
    Directly stated in the abstract as the motivation for proposing LEAF.
  • domain assumption The recursive retrieval agent system with dual-agent cross-validation can supply comprehensive and relevant auxiliary text without contamination.
    This is the core mechanism claimed to enable the benchmark and the observed performance gains.

pith-pipeline@v0.9.0 · 5742 in / 1480 out tokens · 45092 ms · 2026-05-20T22:14:27.558089+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    LEAF utilizes a recursive retrieval agent system paired with dual-agent cross-validation to provide comprehensive and relevant auxiliary text for forecasting... a Retrieval Agent conducts multi-round, timeframe-restricted searches... Fact-Checking Module filters the data, retaining only events with consensus on both publication time and factual authenticity.

  • IndisputableMonolith/Foundation/Atomicity.lean atomic_tick unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    To ensure data authenticity and temporal alignment, we utilize a dual-agent cross-validation mechanism... Retrieved events are retained... if and only if two independent fact-checking agents reach a strict consensus regarding their authenticity and public release time.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages

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    “Realization Date” Determination (The “Max Date” Rule): •Identify the chronological timeline mentioned in the text. • Determine the “Realization Date”: the date when thelatestdetail in the text became public knowledge

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    The Verdict (Truth & Timing): • The factual output (True/False) depends ONLY on Factual Accuracy, regardless of the Reference Date. • IF Factual: Return True and set the output date to the Realization Date (from Step 2).(Crucial: Return the actual realization date, even if it is later or earlier than the Reference Date). •IF Fake: Return False. User Promp...

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    Provide a brief reasoning before your final verdict. Output Format: <reasoning>Explain the verification briefly.</reasoning> <factual>True|False</factual> <date>YYYY-MM-DD</date> Figure 8 | The prompt template used for the Fact Check Agent. The system prompt establishes a rigid verification logic to validate historical accuracy and anchor the true realiza...

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    **Output:** <prediction>val1, val2, val3, ..., valN</prediction> Figure 9| The prompt template used for weather time series forecasting

    **WRAPPER:** Wrap the forecasting numbers inside‘<prediction>‘tags. **Output:** <prediction>val1, val2, val3, ..., valN</prediction> Figure 9| The prompt template used for weather time series forecasting. The model is provided with a city’s historical hourly temperatures and a list of meteorological events to predict future temperatures over a specified h...

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    **Assess Price Context:** Evaluate the recent historical price trend to gauge current market expectations (e.g., is the stock currently over-extended, oversold, or consolidating?)

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    **Identify Strong Signals:** Filter the event list to find clear, material catalysts. Disregard generic PR, pending events with unknown outcomes, or irrelevant macro noise

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    exhausted

    **Check for Exhaustion:** Evaluate if the market has already digested the news. If the provided price data shows a clear reaction AFTER the event was released, treat the catalyst as "exhausted" and do not predict continued explosive movements based solely on that event

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    Markets are mostly efficient

    **Random Walk:** Approach predictions conservatively. Markets are mostly efficient. Unless you identify a strong, clear, and unpriced signal, default to a stable outlook, assuming the price will generally maintain its current trajectory without extreme directional shifts. **Analytical Framework:**

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    Gauge the strength and velocity of the current momentum

    **Assess Trend & Momentum:** Evaluate the historical price data to identify the dominant market regime (e.g., aggressive uptrend, heavy distribution, or lateral consolidation). Gauge the strength and velocity of the current momentum

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    Identify signs of trend exhaustion (e.g., long wicks, diminishing body sizes), continuation patterns, or sudden shifts in buying/selling pressure

    **Analyze Price Action (K-line Dynamics):** Decode the candlestick behaviors leading up to the cut-off date. Identify signs of trend exhaustion (e.g., long wicks, diminishing body sizes), continuation patterns, or sudden shifts in buying/selling pressure

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    **Identify Key Levels:** Locate implied support, resistance, or congestion zones based on recent local highs/lows and historical price memory. Assess how the current price is behaving relative to these boundaries

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    Triangulated

    **Project Technical Trajectory:** Synthesize the technical evidence to forecast the most probable price path. If strong momentum or a valid breakout is confirmed, model the directional continuation. If price action signals exhaustion or rejection at a key level, project a structural reversal, mean-reverting pullback, or Random Walk. * **Format:** Comma-se...

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    Markets are efficient here

    **Determine Domain Weighting (Adaptive Logic):** * *For Financial/Economic Events (e.g., Earnings, CPI, Rates):* Prioritize **Market Pricing** (Options, Swaps, Prediction Markets) and **Hard Data** over rhetorical Signals. Markets are efficient here. * *For Political/Geopolitical Events (e.g., Elections, Legislation, Conflict):* Prioritize **Structural Co...

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    Impact Type

    **Synthesize Evidence:** * **Base Rate:** Establish the baseline probability (Consensus). * **Update:** Shift the probability based on specific "Impact Type" evidence provided (Positive/Negative indicators). * **Shock Check:** Do outlier signals (e.g., a surprise war declaration or a sudden CEO resignation) justify allocating probability to "Tail Risk" ou...

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    {tar- get_outcome}

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    Strong Up

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    **Output:** [Brief Analysis] <prediction>...</prediction> Figure 13 | The prompt template used for the 5-class trend forecasting task

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    No other text inside the tags

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    **WRAPPER:** Enclose your final label inside ‘<prediction>‘ tags. **Output:** [Concise Analysis] <prediction>Trend_Label</prediction> User Prompt For Weather Trend Forecasting (5-class) ”’ (Note: The same with 3-class above) ”’ **Trend Classification (Change in Celsius°C):** * **Strong Up:** Change>3°C * **Up:** Change>1.5°C and<=3°C * **Neutral:** Change...

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    No other text inside the tags

    **FORMAT:** Your final prediction MUST be exactly one of these labels: Strong Up, Up, Neutral, Down, or Strong Down. No other text inside the tags

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    25 bps decrease

    **WRAPPER:** Enclose your final label inside ‘<prediction>‘ tags. ”’ (Note: The same with 3-class above) ”’ Figure 14 | The prompt templates used for the 3-class and 5-class weather trend forecasting tasks. Both setups share an identical structure, differing only in how the user prompt defines the task. {temp_type}specifically denotes the minimum temperat...

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    consensus +50,000), with the August figure revised downward to -3,000, marking a sudden contraction in hiring

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    **Analysis Instructions:**

    [2025-10-10] The Bureau of Labor Statistics (BLS) announced that the September CPI report, originally scheduled for Oct 15, would be delayed and rescheduled for release on October 24 due to the government shutdown. **Analysis Instructions:**

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    **Policy Anchor (SEP):** The most critical piece of evidence is the FOMC’s Summary of Economic Projections (SEP) released on September 17, 2025 [98, 99]. The median participant projected the federal funds rate to end 2025 at **3.6%**. With the target range set at 4.00%-4.25% after the September cut, the midpoint is 4.125%. Reaching 3.625% requires exactly...

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    higher for longer

    **Market Pricing:** As of October 1, market pricing (swaps/futures) indicated a total of ** 48 basis points** of easing remaining for the year [122]. This pricing aligns almost perfectly with the SEP’s implied path of two 25 bps cuts. If the market feared a recessionary crash necessitating 50 bps cuts, or a "higher for longer" pause, this number would dev...

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    downside risks

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    data vacuum

    **Operational Constraints (Shutdown):** The federal government shutdown began on October 1, creating a "data vacuum" (no NFP, delayed CPI) [121, 126]. In the absence of official government data to confirm a crash or a resurgence of inflation, the Fed is highly likely to stick to its communicated baseline (the SEP path). A 25 bps cut represents the "autopi...

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    grad- ual

    **Conclusion:** The convergence of the Fed’s own projection (SEP), market pricing, and the strategic incentive to remain "grad- ual" amidst a data blackout makes a 25 bps cut the overwhelming base case. <prediction>88%</prediction> Figure 15 | A case study of event-augmented forecasting for the Federal Reserve’s interest rate decision. The response demons...

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    [2]2025-11-04: Palantir Technologies (PLTR) stock plunged 7-9% despite beating earnings estimates, triggering a sector-wide sell-off in AI-linked equities

    2025-11-04: Jefferies analyst Blayne Curtis named Broadcom a ’Top Pick’ ahead of earnings. [2]2025-11-04: Palantir Technologies (PLTR) stock plunged 7-9% despite beating earnings estimates, triggering a sector-wide sell-off in AI-linked equities

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    2025-11-24: Federal Reserve Governor Christopher Waller signaled support for a December interest rate cut, fueling a broad ’risk-on’ rally in the technology sector

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    **TASK:** Predict the 3-day cumulative Rate of Change (ROC) category based on the data above

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    **FORMAT:** You must output EXACTLY ONE of the following three categorical labels: "Up", "Neutral", or "Down". NO other words, numbers, or punctuation inside the tags

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    **WRAPPER:** You MUST wrap the exact label inside ‘<prediction>‘ tags. **Output:** [Brief Analysis] <prediction>...</prediction> Trend Forecasting Response From Gemini-3.1-pro The reported negotiations for Meta to utilize Google’s TPUs (manufactured by Broadcom) serve as a significant fundamental catalyst, validating Broadcom’s custom silicon dominance an...

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    a ’ridiculously resilient ridge’ of high pressure is stationed over Northern California

    2025-12-08: According to the National Weather Service (NWS)... a ’ridiculously resilient ridge’ of high pressure is stationed over Northern California... driving... stable, stagnant air masses. ... Consequently, San Francisco’s short-term temperature forecast predicts highs restricted to the mid-to-upper 50s, and overnight lows in the mid-to-upper 40s... ...

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    a synoptic pattern shift will terminate a 20-day dry streak

    2025-12-11: According to the NWS San Francisco Area Forecast Discussion... a synoptic pattern shift will terminate a 20-day dry streak... a deepening low-pressure system off the Pacific Northwest coast will drive a strong cold front... forecast to entrain subtropical moisture, generating a moderately strong atmospheric river characterized by heavy precipi...

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    issued an outlook detailing consecutive Atmospheric Rivers (ARs) impacting the Pacific coast

    2025-12-15: The Center for Western Weather and Water Extremes (CW3E)... issued an outlook detailing consecutive Atmospheric Rivers (ARs) impacting the Pacific coast... shifting significant moisture transport southward into the San Francisco region. The resulting stratiform cloud cover and precipitation will compress the diurnal temperature range over the ...

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    initiating robust moisture advection

    2025-12-16: The National Weather Service Area Forecast Discussion outlines a major synoptic pattern shift... initiating robust moisture advection... forecast to deliver 0.5 to 1.0 inches of quantitative precipitation to San Francisco... (... skip)

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    indicate the approach of a significant atmospheric river, characterized as a ’Pineapple Express’ moisture plume

    2025-12-17: Meteorological drivers identified by the NWS San Francisco... indicate the approach of a significant atmospheric river, characterized as a ’Pineapple Express’ moisture plume... signaling heavy cloud cover and moisture advection that will suppress diurnal temperature variations. (... skip)

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    issued an official forecast predicting daytime high temperatures strictly in the high 50s to low 60s Fahrenheit

    2025-12-18: The National Weather Service (NWS) San Francisco... issued an official forecast predicting daytime high temperatures strictly in the high 50s to low 60s Fahrenheit... Overnight low temperatures are forecast to hold in the low to mid 50s Fahrenheit

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    delivering an exact forecast of 2 to 3 inches of rain to San Francisco

    2025-12-18: According to the NWS Area Forecast Discussion, the primary meteorological drivers altering the thermal profile are a deep low-pressure system and an incoming ’Pineapple Express’ atmospheric river... delivering an exact forecast of 2 to 3 inches of rain to San Francisco

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    This cyclonic activity will drive strong warm air advection and maintain dense cloud cover, effectively preventing standard nocturnal radiational cooling [3]

    2025-12-18: The NWS San Francisco Bay Area office highlighted a deepening low-pressure system... This cyclonic activity will drive strong warm air advection and maintain dense cloud cover, effectively preventing standard nocturnal radiational cooling [3]

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    The CPC explicitly forecasts an above-normal precipitation regime for Northern California

    2025-12-18: The NOAA Climate Prediction Center (CPC) published 6-10 and 8-14 Day Outlooks... The CPC explicitly forecasts an above-normal precipitation regime for Northern California... This sustained subtropical moisture transport will lock in extensive cloud cover and elevated dew points over San Francisco (37.7749, -122.4194), severely dampening the di...

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    **BRIEF ANALYSIS:** Provivde a concise reasoning to explain your prediciton

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    **UNIT ALIGNMENT (CRITICAL):** Your final predicted values MUST be in Celsius (°C) to align with the magnitude and distribution of the Historical Hourly Temperatures in Part A

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    No units, no dates, no extra text

    **FORMAT:** Comma-separated values ONLY. No units, no dates, no extra text

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    Pineapple Express

    **WRAPPER:** Wrap the forecasting numbers inside ‘<prediction>‘ tags. **Output:** <prediction>val1, val2, val3, ..., valN</prediction> User Prompt for Context-aided Time Series Forecasting **Brief Analysis:** The meteorological intelligence indicates a significant synoptic pattern shift for San Francisco, transitioning from a cold, dry, and stagnant high-...

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    160 for Yoshihiko Noda) but was forced to form a minority government requiring case-by-case opposition support to pass the budget

    2024-11-11: Japanese Prime Minister Shigeru Ishiba was re-elected in a parliamentary runoff vote (221 votes vs. 160 for Yoshihiko Noda) but was forced to form a minority government requiring case-by-case opposition support to pass the budget. (... skip)

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    2025-05-06: The German Bundestag elects Friedrich Merz (CDU) as Chancellor in a second round of voting with 325 votes (surpassing the 316-seat absolute majority), formally ending Olaf Scholz’s chancellorship. (... skip)

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    2025-06-13: Polymarket trading data for ’First leader out of power in 2025?’ remains active, with Shigeru Ishiba (Japan) eventually settling as the winner (100%) following his resignation in September 2025, despite earlier exits by Trudeau and Scholz in Q1 2025. (... skip) [152]2025-06-17: The G7 Summit concludes without a joint communiqué due to disagree...

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    **Synthesize:** Review the provided *Hard Data*, *Signals*, and *Market Pricing*

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Showing first 80 references.