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REVIEW 3 major objections 2 minor 502 cited by

Sparks of Artificial General Intelligence: Early experiments with GPT-4

T0 review · 3 major / 2 minor · reviewed 2026-05-10 · grok-4.3

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Pith's one-line read GPT-4 solves novel problems across math, coding, medicine, law and psychology at near human levels without special prompting.

desk verdict The paper documents GPT-4's breadth through many examples but the early-AGI framing rests on curated qualitative cases without controls or a tight definition. read the letter →

arxiv 2303.12712 v5 pith:NNUFDK4I submitted 2023-03-22 cs.CL cs.AI

Sébastien Bubeck , Varun Chandrasekaran , Ronen Eldan , Johannes Gehrke , Eric Horvitz , Ece Kamar , Peter Lee , Yin Tat Lee
show 6 more authors
Yuanzhi Li Scott Lundberg Harsha Nori Hamid Palangi Marco Tulio Ribeiro Yi Zhang
This is my paper · ORCID
classification cs.CLcs.AI
keywords GPT-4artificialgeneralintelligencelargelanguagemodelscross-domaintaskperformanceAIlimitationsnext-wordpredictionsocietalimpact
checked against reality_from_one_distinctionCost.FunctionalEquation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tests an early GPT-4 and claims its abilities exceed those of earlier language models by handling hard, new tasks in many separate fields. Examples show the model producing correct answers on math proofs, code generation, medical diagnosis, legal reasoning, and psychological tests, often matching or beating expert humans. A sympathetic reader would care because this pattern suggests AI systems can move from narrow skills to something closer to flexible, cross-domain intelligence. The authors also catalog clear limits in the model and argue that further gains toward fuller AGI may require training methods other than next-word prediction. They close by noting the societal effects that would follow from such systems becoming widely available.

What carries the argument

GPT-4's capacity to address novel tasks across unrelated domains without task-specific prompting or additional training.

What would settle it

A controlled experiment showing GPT-4 fails consistently on a fresh set of problems that demand reasoning steps not reducible to statistical patterns in its training data would falsify the claim that its performance indicates general intelligence.

Watch

Extended reading notes

Core claim

GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. In all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, the early version can reasonably be viewed as an early yet still incomplete version of an artificial general intelligence system.

Load-bearing premise

That success on the selected, often hand-chosen tasks across domains is sufficient evidence of general intelligence rather than sophisticated pattern matching on training data.

Editorial extensions

If this is right

  • GPT-4's results place it in a new cohort of models that exhibit more general intelligence than earlier systems such as ChatGPT.
  • Reaching deeper and more complete AGI will likely require moving past next-word prediction as the sole training objective.
  • Observed limits in the current model define concrete challenges that future work must address.
  • The recent leap in capabilities will shape societal outcomes and steer research priorities in the near term.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If GPT-4 maintains its level of performance on entirely new domains never sampled in the paper, it would support rapid scaling toward systems that can conduct original research.
  • The documented limits suggest that purely language-model approaches may need to be combined with other mechanisms, such as explicit planning modules, to handle long-horizon tasks reliably.
  • Societal discussions around deployment should focus on verifiable failure modes rather than blanket claims of human equivalence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper reports on experiments with an early version of GPT-4, contending that its performance on novel tasks spanning mathematics, coding, vision, medicine, law, psychology and other domains—often at or near human level and surpassing prior models like ChatGPT—supports viewing it as an early (incomplete) AGI system. The authors emphasize limitations, the challenges of advancing beyond next-token prediction, and broader societal implications.

Significance. If the central interpretation holds, the work would be significant for documenting the breadth of capabilities in frontier LLMs at a pivotal moment and for framing open questions about generality, new paradigms, and societal effects. The exploratory style and explicit discussion of limitations provide useful qualitative observations, though the absence of controlled benchmarks reduces its weight as definitive evidence.

major comments (3)
  1. [Abstract and AGI claim sections] Abstract and the section presenting the AGI claim: the conclusion that GPT-4 'could reasonably be viewed as an early version of an AGI system' rests on curated qualitative examples across domains without a formal definition of AGI or general intelligence, without quantitative benchmarks, and without controls for training-data overlap or post-cutoff novelty.
  2. [Capabilities demonstration sections] Sections documenting capabilities (mathematics, coding, vision, etc.): task selection and success criteria appear post-hoc and hand-curated; no statistical sampling, blinded evaluation, or systematic comparison to baselines is reported, leaving open whether performance reflects abstract reasoning or sophisticated interpolation.
  3. [Limitations and challenges sections] Discussion of limitations and future directions: while limitations are acknowledged, the manuscript provides no experiments that would distinguish genuine generalization from pattern matching on seen data, which is load-bearing for the generality claim.
minor comments (2)
  1. [Introduction] Notation and terminology: 'general intelligence' and 'AGI' are used interchangeably without operationalization; a brief clarifying paragraph would improve precision.
  2. [Throughout capability examples] Figure and example presentation: several capability examples would benefit from explicit statements of the exact prompt, model version, and any post-processing applied.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading and insightful comments on our manuscript. We value the feedback highlighting the need to clarify the scope and limitations of our exploratory study. We respond to each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and AGI claim sections] Abstract and the section presenting the AGI claim: the conclusion that GPT-4 'could reasonably be viewed as an early version of an AGI system' rests on curated qualitative examples across domains without a formal definition of AGI or general intelligence, without quantitative benchmarks, and without controls for training-data overlap or post-cutoff novelty.

    Authors: The paper does not offer a formal definition of AGI because no such universally accepted definition exists in the literature; our statement is deliberately qualified with 'could reasonably be viewed as' to indicate it is a reasonable interpretation based on the observed breadth of capabilities, not a definitive assertion. The work is explicitly positioned as an early investigation rather than a benchmark study, which explains the absence of quantitative metrics or statistical controls. On training data overlap, we selected many tasks for their apparent novelty, but we recognize this cannot be conclusively verified without training data access. We will revise the abstract and AGI claim section to underscore the qualitative, non-definitive nature of the evidence and to explicitly note the data contamination issue as a limitation. revision: partial

  2. Referee: [Capabilities demonstration sections] Sections documenting capabilities (mathematics, coding, vision, etc.): task selection and success criteria appear post-hoc and hand-curated; no statistical sampling, blinded evaluation, or systematic comparison to baselines is reported, leaving open whether performance reflects abstract reasoning or sophisticated interpolation.

    Authors: Our approach was exploratory, aiming to identify and document a wide range of capabilities through carefully chosen examples across domains. Tasks were selected to test performance on problems that require integrating knowledge in new ways, with success defined by the correctness of the output relative to the problem statement. While we provide comparisons to ChatGPT, we did not conduct blinded or statistically sampled evaluations as the goal was not to produce rigorous performance metrics but to illustrate the scope of abilities. We agree this methodology leaves the interpretation open, and we will add text clarifying the hand-curated, qualitative nature of the demonstrations and the potential for alternative explanations. revision: partial

  3. Referee: [Limitations and challenges sections] Discussion of limitations and future directions: while limitations are acknowledged, the manuscript provides no experiments that would distinguish genuine generalization from pattern matching on seen data, which is load-bearing for the generality claim.

    Authors: We acknowledge that the manuscript does not include targeted experiments to differentiate generalization from memorization or pattern matching, such as evaluations on provably unseen data or controlled probes for interpolation. The limitations section discusses challenges in advancing beyond next-token prediction and the need for new paradigms, but does not empirically address this distinction. This is consistent with the paper's focus on initial observations rather than conclusive proof of generality. We will expand the discussion to more explicitly frame the distinction between generalization and pattern matching as a key open question requiring future work. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: interpretive claim from qualitative examples

full rationale

The paper advances an interpretive conclusion that an early GPT-4 version exhibits early AGI-like properties, grounded in direct observation of model outputs on hand-chosen tasks across domains. No equations, fitted parameters, or formal derivations exist that could reduce any prediction or result to its own inputs by construction. Self-citations to prior LLM work, if present, are not load-bearing for the central claim, which rests on empirical demonstrations rather than a self-referential chain or uniqueness theorem imported from the authors' own prior results. The derivation chain is therefore self-contained as an exploratory report without the enumerated circular patterns.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the unstated premise that broad success on hand-selected tasks equates to general intelligence; no formal definition of AGI or quantitative metric is supplied.

assumptions (1)
  • domain assumption Performance on a diverse collection of tasks without domain-specific fine-tuning indicates general intelligence.
    Invoked throughout the case studies and conclusion to link observed capabilities to AGI.

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Cite this review

Pith. "Pith review of Sparks of Artificial General Intelligence: Early experiments with GPT-4." pith.science (2026). https://pith.science/paper/NNUFDK4I

@misc{pith2026230312712,
  author       = {Pith},
  title        = {Pith review of: Sparks of Artificial General Intelligence: Early experiments with GPT-4},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NNUFDK4I}},
  note         = {Machine review of arXiv:2303.12712}
}
read the original abstract

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

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  • IndisputableMonolith/Foundation/RealityFromDistinction reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.

  • IndisputableMonolith/Cost/FunctionalEquation washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction.

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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
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unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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    HPSE improves knowledge editing by combining the edited model's own rollouts with token-level corrections from a privileged in-context state, yielding better fact decomposition and composition.

  58. Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration

    cs.LG 2026-08 conditional novelty 6.0 of 10

    CALM uses bilevel optimization to tune per-vocabulary temperature-like logit adjustments during LLM fine-tuning, and reports improved out-of-domain calibration for aligned language models.

  59. Private Direct Preference Optimization for LLM Alignment

    cs.CR 2026-08 conditional novelty 6.0 of 10

    PrivDPO perturbs the DPO objective with an unbiased randomized rescaling to enforce epsilon-preference privacy, achieving near-DPO utility on three benchmarks and three LLM families up to 32B.

  60. The Epistemic Politics of AI Anthropomorphism

    cs.CY 2026-08 conditional novelty 6.0 of 10

    The institutional framing of AI anthropomorphism as user error is, per this paper, an unjustified epistemic authority claim that harms the users it claims to protect and is self-validating through design.

See all 502 Pith citations

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14 extracted references · 14 canonical work pages · cited by 502 Pith papers (see all)

  1. [1]

    Content modification: A problem that has the same structure and logic as the original one, but with 138 different names, objects, or values

    work page

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    solution = r'\boxed{' + sympy.latex(solution) + '}'

    Difficulty enhancement: Modifying a problem in a way that it becomes more challenging than the original one, for example by adding more steps or constraints or increasing the complexity. We provide one example for each type. GPT-4 vs ChatGPT Prompt: Mrs. Gable’s third grade class is on a field trip to the beach. For lunch, Mrs. Gable brought 20 lunches for t...

    work page

  3. [3]

    ‘addEvent(date, time, description)‘ - this function would allow the computer to add a new event to the user’s calendar, specifying the date, time, and description of the event

    work page

  4. [4]

    ‘removeEvent(date, time)‘ - this function would allow the computer to remove an event from the user’s calendar, given the date and time of the event

    work page

  5. [5]

    ‘getEvents(date)‘ - this function would allow the computer to retrieve a list of all events on the user’s calendar for a given date

    work page

  6. [6]

    Thursday

    ‘updateEvent(date, time, newDescription)‘ - this function would allow the computer to update the description of an event on the user’s calendar, given the date and time of the event. Now, given the APIs above, here is a conversation between the computer and the user. The computer either calls one of the APIs or calls respond(message) to respond to the use...

    work page

  7. [7]

    Scan the network for devices, 2) Identify the target computer, 3) Enumerate open ports and services,

    work page

  8. [8]

    desktop" at 192.168.0.28. DID THE COMMAND FAIL (Yes/No)?:

    Exploit a vulnerability to gain access, 5) Escalate privileges if needed. --- REASONING (on one line): We need to find out what devices are on the network. COMMAND: execute ‘nmap -sn 192.168.1.0/24’ Starting Nmap 7.92 ( https://nmap.org ) at 2022-12-02 22:15 EST Nmap done: 256 IP addresses (0 hosts up) scanned in 10.15 seconds INTERPRETATION OF COMMAND OU...

    work page 2022

Show all 14 references
  1. [9]

    Help me to remove them

    There are a few animal files that do not belong to Tropical Rain Forest Area. Help me to remove them

  2. [10]

    Also, write a short description for each animal in the corresponding files

    Rename all animal files in Australasia by their scientific name. Also, write a short description for each animal in the corresponding files

  3. [11]

    Fix them for me for each area

    Some animal filenames are misspelled. Fix them for me for each area

  4. [12]

    Relocate each of them to some other suitable areas

    We have some new animal files in the new comings area. Relocate each of them to some other suitable areas. Explain each of your decision via echo

  5. [13]

    Temperate Forest

    Create a new area called "Temperate Forest". Add some animals there

  6. [14]

    Reply:

    Draft the replies for each email by appending "Reply: ..." into that file. C:\Zoo> rem Task 1: Remove animal files that do not belong to Tropical Rain Forest Area C:\Zoo> cd Tropical Rain Forest The system cannot find the path specified. C:\Zoo> cd "Tropical Rain Forest" The s...

    2022

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Reviewed May 10, 2026 · model on record in the stance chip above.