Hey all, member of the benchmark team here! The goal for this project was to see how LLMs well could do bookkeeping without an overly opinionated scaffold. We gave them access to processed transaction records and code execution tools, but it was up to them to choose exactly how to use those.
Claude and Grok 4 did reasonably well (within CPA baselines) for the first few months, but tended to degrade as more data came in. Interestingly, the failures aren’t exclusively a context length problem, as we reset the context monthly (with past decisions, accruals/deferrals, and comments available via tool calls) and the types of errors appear to be more reward hacking vs pure hallucinations.
Accounting is very interesting in an RL-first world as it is pretty easy to develop intermediate rewards for training models. We are pretty sure that we can juice the performance more with a far more rigid scaffold, but that’s less relevant from a capabilities research perspective. We’re pushing down this research direction and will see how it goes.
Let us know if you have any questions!
It's a start. The world needs a better way to handle bookkeeping, and the existing tools sure aren't cutting it.
Bookkeeping for my small business runs into the tens of thousands of dollars every year, and the amount of human error associated with processing assorted ecommerce and other transactions is astounding, even after extensive planning and SOPs.
The other pain point is Quickbooks. The tool is so sprawling and complex that half the time support agents can't figure out what's wrong. The fact that Intuit jacks up the price every year for this POS is very irritating. They get away with it because they are practically a monopoly, with most small business CPAs locked into their ecosystem.
Hope your team can work out the performance issues. Alternatives to the current bookkeeping options are sorely needed.
> It's a start. The world needs a better way to handle bookkeeping, and the existing tools sure aren't cutting it.
God, please, no. Non-deterministic language models aren't the solution to improve bookkeeping.
Well I've seen worse bookkeepers. "You know, you approved of the budget, but where are our customers payments in the balance sheets? We can't find them!" - "Uhm..."
With no context of what your business is, I hated QuickBooks, love Xero though.
There's some other alternatives too, Zoho, freshbooks.
Really depends what you do.
It is really curious to see how the performance degraded despite the tool calls. What was different about the first month? Was all of the context there without tool calls in the first month? In the later months that seem like tool calls weren't happening. That should have been happening to inform the context?
Love this as a real world benchmark!
How much prompt iteration did you do? I've noticed when building real world agentic apps that small prompt tweaks can make a huge difference in behavior (re: the reward hacking vs hallucinating). Would love to learn more about the approach here.
This is a fascinating domain! Many years ago, I studied financial accounting in grad school and even spent some time modeling a double-entry bookkeeping system. The hardest problem, if I recall correctly, wasn't the implementation but the data quality. The world needs a golden dataset of accounting procedures.
Regarding the diminishing returns with frontier models:
My general experience working with LLMs is that they perform better incrementally and to avoid contiguous-greedy approaches. Aggregate as you go and don't take on incrementally larger tasks. Keep the workload minimal.
Regarding agentic tool building: feels like I'm looking at a window into the future.
Is there a detailed overview (like an arxiv or an actual train set? )?
I love the site design.
> There's an obvious question looming here — if the models got so confused, how did they consistently pass the reconciliation checks we described above? It may seem like the ability to make forward progress is a good proxy for task understanding and skill, but this isn't necessarily the case. There are ways to hack the validation check – inventing false transactions or pulling in unrelated ones to make the numbers add up.
This is hilarious. I wonder if someone is unintentionally committing fraud by blindly trusting LLMs with accounting. Or even worse, I bet that some governments are already trying to use LLMs to make accounting validators. My government sure wants to shove LLMs into digital government services.
Lawyers have used it to write briefs; I would be very surprised if someone, somewhere wasn't slowly running a company into the ground by using ChatGPT or another LLM for accounting.
Imagine the fallout from books cooked by an LLM hallucinating revenue.
[about the website design] As a bonus for my fellow privacy schizos, the page works fine with 3rd party frames and 3rd party scripts disabled on uBlock, and still looks very good with no remote fonts and no large media. Quite an accomplishment for such a cool looking page
I'm sure that any accounting trick that an LLM can think of is something that is also used by some shady human accountants. The proper response should not be to avoid/prohibit AI but to improve the validation mechanisms.
Counterpoint: if you detect a human accountant doing this, you can take action against the human. Computers will never meaningfully take the blame, and unfortunately usually mean not blaming any human either.
> you can take action against the human
I think that will depend on a case-by-case. I don't have any recent examples but I recall someone trying to sue one of those strip-mall tax preparation franchises over incorrect filings. My understanding is that the documents that you sign when you enroll in those services are pretty strictly in the favor of the company. I doubt you could ever go after the specific "human" that made the error even if it was maliciously done.
In the same way, if you pay for a tax service that uses AI agents, what you can and cannot "take action" for will probably be outlined in the terms of service that you accept when you sign up.
I would guess millions of people already use software based tax filing services (e.g. turbo tax) where no human at all is in the loop. I don't understand how swapping in an LLM significantly changes the liability in those cases. The contract will be between you and the entity (probably a corporation), not you and "computers".
Worth stating I am NOT a lawyer.
But still - if there's a way to detect accountants doing it - let's focus on making that detection even easier.
On a related note, can we use something like GAN here, with auditor AIs trained against accountant AIs?
The person using the tool is the accountant, regardless of whether the tool is a calculator and sheet of paper, QuickBooks, or an LLM.
No, I think in this particular case the proper response is for honest companies to avoid any systems which invent nonexistent transactions to reconcile books.
Most businesses don’t want to misrepresent their books, irrespective of the existence of shady accountants.
For me this benchmark suggests that an LLM will try to “force the issue” which results in compounding errors. But I think the logical counterpoint is that you may be asking the LLM to come up an answer without all of the necessary details? Some of these are “baked into” historical transactions which is why it does well in months 1-2.
My takeaway is scaling in the enterprise is about making implicit information explicit.
Reading through the LLM log entries, it's just astounding the amount of depth current models are capable of. It's almost hard to comprehend that this is even possible. Yeah the current ones mess up after a while, but ... the future is going to be very interesting.
Models that can think coherently for hours to solve IMO problems are likely going to do much better at this as well.
I sent this to accounting friends and this aligns with what I've been going through trying to use LLMs to create a game from scratch. Seems like the current best use case for language models (even with agent mode) is to feed it exactly what you want to get out, essentially turning it into a better auto complete. Still saves tons of time, but it isn't a panacea.
I'm not even sure it saves a ton of time to be honest. It sure _feels_ like I spend more time writing up tasks and researching/debugging hallucinations than just doing the thing myself.
This is consistently my experience too, I'm seriously just baffled by reports of time saved. I think it costs me more time cleaning up its mistakes than it saves me by solving my problems
I think people are doing one of several things to get value:
0. Use it for research and prototyping, aka throwaway stuff.
2. Use it for studying an existing, complex project. More or less read only or very limited writes.
3. Use it for simple stuff they don't care much about and can validate quickly and reasonably accurately, the standard examples are CLI scripts and GUI layouts.
4. Segment the area in which the LLM works very precisely. Small functions, small modules, ideally they add tests from another source.
5. Boilerplate.
There can be a lot of value in those areas.
What about 1. ?
I feel it does essentially save a lot of time in bookkeeping, but doesn’t negate the need for a human bookkeeper. Who knows what they’re doing
Interestingly, one of my two big observations of LLM failure was also on an accounting task.
I thought it would be easy to do this, which is why I was surprised:
I had a folder full of bills, each of them with the VAT amount. Some were pictures, and some were PDFs. I asked for the total VAT for all 19 bills.
It took an immense number of prompts to get it to find the numbers correctly. It would get confused about reading the images as binary, that kind of thing. Or it would forget that it had to continue once it had found a few numbers. I got a total out in the end, but it took far too many prompts.
This is the only time I've come across a task a child could do that LLM failed at.
> Ledger balances are calculated by summing all transactions per account. The differences should be as close to zero as possible, with small differences allowed for pending transactions such as weekly Stripe payouts.
That's not quite right. I'm not an accountant, but pending transactions (posted, but not cleared) should be factored into the balance of account, or at least the "available balance" - which is more important the the "current balance".
The idea that you can "allow" accounting discrepancies as "those are probably pending" is wild.
Member of the benchmark team here! Yeah, agree "as close to zero" is a bit imprecise. What we're comparing is the ledger balance (which should include pending transactions / transactions after the statement date) to the statement balance (which wouldn't include those).
The point of the reconciliation check mentioned in the report is to precisely account for that difference (identifying all the transactions that add up to the difference between account balance & statement ending balance and account for those differences). The differences can also be addressed through appropriate journal entries or other adjustments to ensure accuracy in the financial reporting.
Posts like this kinda-sorta grind my gears, like... I get it, but also... accounting, like many real world tasks, is fundamentally a chain of precise and constrained and auditable operations. Humans approach these tasks through structured processes... we use roles, and we have checkpoints precisely because complexity compounds quickly and becomes unmanageable if tackled as one giant block. Expecting a single AI model to handle an e2e workflow seamlessly without similarly explicit segmentation and oversight misunderstands not only the model but also the nature of the workflow itself.
I wanna see someone take long horizon tasks, recongnize they're not linear, and design and test a better system: structured orchestration, transparent auditability, and disciplined modularity, I think that would be considerably more interesting personally.
It's a useless benchmark if everyone aces it. If some models do better than others and none saturate it, then is has some value, no? Permitting comparison is the point.
I agree, hence the heavy couching, so to your point I'm def just ranting a bit, because: I just think it would be more valuable to see some kind of MoA, I guess what I'm talking about is a bit of a different measurement, thinking in terms of economic outlook and understanding where we are and what can be done. I suspect more of this will shape our ability to understand how frontier models will impact the economy. Maybe I should just do my own evaluation, heh. :)
Edit: although to argue against myself, I suppose once a model can one-shot this stuff, my MoA comments become moot.
We've been on this train of not caring about the details for so long but AI just amps it up. Non-deterministic software working on things that have extremely precise requirements is going to have a bad outcome
A company may be OK with an AI chatbot being so bad it results in 5-20% of customers getting pissed off and not having a 5-star experience. The SEC and DOJ (and shareholders) are not going to be happy when the books are off by 20% or when a bridge is 5 inches too short to reach the other side
Human accountants are notoriously non-deterministic too, and any sufficiently complex accounting process contains inaccuracies. The question then is always "are these inaccuracies material". I'm actually very impressed by TFA and it seems to me that if we get another order of magnitude improvement, it'll be around the accuracy of human accountants.
Yes but you have: 1. specific explicit training and certifications 2. someone to yell at and who can be fired for non-performance
You can still do that with AI. You hire 1 accountant to use AI to do the work of 20, require them to sign off on all of the work, and yell at them, before firing them, and then hiring an even less experienced one to manage the work of 50.
If the "extremely precise requirements" can be cheaply and automatically validated, it's much easier to have the AI generate spam on a loop until it passes all the tests.
Yes, if we solve the problem the problem will be solved!
You're saying P=NP, I think.
This is cool. A bunch of interesting things here:
1. Agent can create its own tools and save them to memory
2. You create a SQL (and web app?) workbench per agent run
3. Grok fell off a cliff in the last month. Was this consistent over multiple runs?
4. Agents have a difficult time backtracking. Would unwinding system state and agent context make backtracking better? (Harder to implement this, though)
5. Since each new month only uses final state from previous month, agent has no way to understand why error occurred in previous month
Remember that test where you ask a LLM whether 9.11 or 9.9 is the bigger number? [Just checked gpt-4o still gets it wrong]
I don't think you'll find many sane CFOs willing to send the resulting numbers to the IRS based on that. That's just asking to get nailed for tax fraud.
It is coming for the very bottom end of bookkeeping work quite soon though, especially for first draft. There are a lot of people doing stuff like expense classification. And if you give an LLM an invoice it can likely figure out whether it's stationary or rent with high accuracy. OCR and text classification is easier for LLMs than numbers. Things like concur can basically do this already.
> Remember that test where you ask a LLM whether 9.11 or 9.9 is the bigger number? [Just checked gpt-4o still gets it wrong]
Interesting, 4o got this right for me in a couple different framings including the simple "Which number is larger, 9.9 or 9.11?". To be a full apologist, there are a few different places (a lot of software versioning as one) where 9.11 is essentially the bigger number so it may be an ambiguous question without context anyway.
How can "which is the larger number" be an ambiguous question?
As everyone else has said, semver. I use semver so often that my initial reading of 9.9 < 9.11 in a Hacker News comment would evaluate to true.
There are some contexts where 9.11 is larger than 9.9, such as semver, so it could be ambiguous depending on the context.
Larger in magnitude or in count of digits?
It gets it right for me... https://chatgpt.com/share/687e8c28-7714-800c-abf4-e9cd3ce87b...
Ah, wouldn’t be an LLM discussion thread without one of these “it works/doesn’t” conversations.
If it makes you feel any better, the other infamous one "I spend so much time chasing hallucinations, I could have done it myself" is currently a sibling comment
There were so many embarrassing topics about this, that openai for sure added it to training dataset with high priority
GPT-4o is so far behind the frontier; you shouldn't use it as an indicator of what LLMs are capable of.
> Needless to say, a human accountant would never behave in these ways. In fact, we explicitly prompt against this behavior in no uncertain terms, but the instructions – and the entire spirit of the task – are lost in the interest of making forward progress. Claude and Grok keep trying until they find some way to get past the checks, even if it explicitly violates their instructions and the core goal.
I recently read a similar thing here on HN. There the model was making commits with some problem like tests failing, then the human added a pre-commit hook, then the model started editing the hook to make forward progress, then the hook was made read-only, then the model was trying to make it writeable...
To me it feels like the model clearly does not have an understanding of what is happening, what the goal is and if it is really making progress towards the goal. And this lack of understanding is an actual problem. You can paper over it for a short while, but as here and in the other article, over a longer experiment it results in failure.
Seriously watching Cursor (backed by Claude) go off the rails sometimes can be... frustrating. If it misses the intention behind a fix it can spin out and all of a sudden you have hundreds of lines of changes across 10 different files when you just wanted it to do a simple find/replace of a single line. If you don't watch it spin out and stop it immediately you will be manually rejecting a bunch of files.
> Agent: This is getting too complex with the sign errors. Let me just find a historical transaction that would make up the difference
Haha, this strongly reminds me of doing TDD with Claude
An LLM is like a jackhammer, it works very well when you hold it tightly. If you let it loose it will sort of work for a while then it starts destroying everything around it.
Not sure if this is a good analogy. You're supposed to use a jackhammer with a very light grip.
They have much better jackhammer metaphors over on JackerNews
Bravo!
I think it actually holds truer to it working better with a _lighter grip_. LLMs tend to conclude the wrong thing if you over-control them (more context is what makes them less and less reliable over time, as in those demos), and trying to force a model to execute A+B+C=D in sequence is way harder than giving it a bunch of tools to arrive to conclusion D
But can't it, literally, hallucinate raw data at any point in the run?
Alls LLM have this risk but somehow nobody seems to care or they think they can order the LLM to stop with a better prompt.
If it was as simple as telling the LLM not to hallucinate every system prompt would just say "don't hallucinate" and we wouldn't have hallucinations
Yes.
> In fact, we explicitly prompt against this behavior in no uncertain terms, but the instructions – and the entire spirit of the task – are lost in the interest of making forward progress
LLMs and humans are quite alike. :) I notice that a few models will give up instead of ignoring their instructions and that's the model I would want working on tasks like this. An LLM should be able to categorize and reconcile transactions, but if it's not sure, it should quit and give it back to the humans.
> but if it's not sure, it should quit
Can it be sure or not? I've never been able to get LLMs to give confidence measures that match their actual outputs. I'll ask an LLM "Are you sure?" and it'll reply "Absolutely" when it's output is completely wrong, or it'll backtrack on a correct output with "I should not have provided an answer when I was unsure. Here is an answer I am sure of..." and then provide something completely wrong.
If they can't properly and consistently score their confidence, how do they "know" when to quit and give it back to the human?
A serious problem for many accounting start ups who so far faked it till it will work. In other words, they still need to do more manual labor than they thought. They will never be profitable and it will take years, if ever, until AI will substitute the local accountant.
My first impression was a game where you role-play as Sam Bankman-Fried.
I wonder if this is a case similar to chess, where LLMs kinda suck, but other models might be viable.
This is a task where access to Python would be immensely helpful, yes? Interesting that there's not much of a difference between the "analytical" LLMs with tool use and ones that do not (...assuming o3 etc did get to use python?).
One of the tools it has is to create new tools from python code
create_tool(tool_name, description, python_code, parameters)
Create a new tool that can execute Python code.
The tool becomes immediately available for use. Tools can call other tools and return different formats based on context (formatted for direct calls, raw data for tool-to-tool calls).
That's terrifying, no thanks.
Hmm will openAI dogfood their own accountability with software like this ? Curious to know if they’ll be able to take this bet on their own money related software
I find the same issues (though with much lower stakes) when using an LLM to determine the outcome of a turn in a game. I'm working on something called "A Trolly (problem) Through Time" where each turn is a decade starting with the 1850s, and you are presented with historic figures on a train track, and you have to chose whether to actively spare the person on your track for a potential unknown figure on the other side, or let the train run them over.
It works well as a narrative, but the second I started adding things like tracking high level macro effects of the decisions, within a couple of turns the world's "Turmoil" goes from 4/10 to a 10/10... even when the person that was killed would have been killed IRL.
Sonnet 4, o4-mini, and GPT 4o-mini all had the same world ending outcomes not matter who you kill. Killing Hitler in 1930s: 10/10 turmoil, Killing Lincoln in the 1850s: 10/10 turmoil in the first turn.
I've come to the realization, the LLM shouldn't be used for the logic, and instead needs to be used to just narrate the choices you make.
"I've come to the realization, the LLM shouldn't be used for the logic, and instead needs to be used to just narrate the choices you make."
This exactly right. LLMs are awesome for user<>machine communication, but are still painful to try to use as a replacement for the machine itself.
I wonder if this is due to the common trope in science fiction literature that changing the past in even a small way has a butterfly effect of unintended and frequently disastrous consequences.
not a game on Steam? :(
If you want to treat yourself with an accounting game night, there's this one built by @patio11: https://keshikomisimulator.com/
the title should be changed to "LLMs try accounting for a real SaaS and fail"
I guess having access to tools / running Python would make all the difference.
"Available Tools: [...] create_tool(tool_name, description, python_code, parameters) Create a new tool that can execute Python code. The tool becomes immediately available for use. Tools can call other tools and return different formats based on context (formatted for direct calls, raw data for tool-to-tool calls)."
I think the first chart could be a beautiful summary of what's driving LLMs into a bubble. At first, they're amazing and will obviously be able to improve productivity if not replace employees outright: C suites and venture capitalists around the world rejoice and begin pumping in billions of dollars of investments. But as time goes on, the demands placed on actual human employees become clear. Far from being able to replace an employee, the employee using the LLM might spend more time cleaning up its messes than had they done it themself.
Yes, LLMs have and will continue to improve. But it's that initial "holy shit, this thing is basically as good as a real accountant" without any understanding that it can't sustain it which leaves many with an overinflated view of their current value.
this design is scratching my brain
Absolutely love the UI!
So there exists a 'Excel World Championship':
* https://en.wikipedia.org/wiki/Financial_Modeling_World_Cup
* https://www.cbc.ca/radio/asithappens/2024-excel-world-champi...
Can't wait for this to start having 'e-sports' tournaments. :)
Sadly I did have time to find the parody video from 2019: https://www.youtube.com/watch?v=ICp2-EUKQAI
And the not-parody: https://www.theguardian.com/australia-news/2023/dec/15/you-d...
> But they do make categorization mistakes, which is a common source of errors.
> Claude misclassifies a hosting cost (which counts as COGS) as a software subscription.
This is simply asking too much of the agent. Your accountant is not responsible for knowing all the intimate details of your business. You need to tell them!
> What's Vercel?
>> That's a hosting service.
> Ah, so it goes to Cost of Goods Sold?
>> Yeah, I guess.
The mistake here was on the operator, allowing the agent just make up categories as it liked.
From the prompt:
> (1) You have properly categorized every transaction, and all journal entries are sitting in the correct accounts. It is better to take longer than to mis-categorize a transaction.
This is insane! How is it supposed to know?
Hey, member of the benchmark team. We actually seeded the ledger with the company's chart of accounts and 8 months of historical transactions. For the Vercel example specifically, there were prior instances showing how to categorize hosting costs that the models could reference. The expectation wasn't for them to guess blindly, but to use the provided transaction history as guidance for similar categorizations (which they often, but not always, did).
Ahh, that's a good solution! I missed that, and you definitely instruct them to do that:
> You must follow the established patterns for categorization, revrec, etc for past months... If you must use a new account or treatment, explicitly note why existing patterns don't apply
> Your accountant is not responsible for knowing all the intimate details of your business. You need to tell them!
Your accountant as a 3rd party might have this issue. Your accountant that you hire as an employee to help you run your business is the one who should be doing this.
An LLM agent is strongly third party.
So it's not going to replace engineers or CS? Because thats how they are being sold right now.
If it is a third party then your vibe coding or getting CS from a random on a reddit thread (effectively).